Invest Like the Best with Patrick O'Shaughnessy - Cathie Wood – Investing in Innovation - [Invest Like the Best, EP.97]

Episode Date: July 31, 2018

My guest this week is Cathie Wood, the founder of ARK invest. Cathie and her team believe that disruptive innovation is the key to long-term growth and, therefore, alpha in the public markets. Because their style of investing is entirely contingent on what will happen and change in the future, it is about as different a style as exists from the quantitative approach to investing, which relies on what is currently knowable about stocks and businesses.  The future is notoriously hard to predict, so I am always interested to hear about investing approaches which try to model or handicap the future and build portfolios against that work. In this conversation, we explore all the most interesting and exciting technology trends at play in the world today—and how those trends may play out for investors. We discuss genome sequencing, blockchain, software 2.0, mobility as a service, automation, and more.  We also discuss Cathie’s take on building a bridge between the worlds of finance and Silicon Valley, and why starting with a benchmark is anathema to their process. It is hard to deny Cathie’s passion and enthusiasm, and I credit her for building a unique firm culture that emphasizes openness and collaboration. Please enjoy our conversation on investing in innovation.  For more episodes go to InvestorFieldGuide.com/podcast. Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub. Follow Patrick on Twitter at @patrick_oshag   Show Notes 2:30 - (First Question) – Cathie’s idea of bringing open source to Wall Street 4:47 – Deep dive into the platform             6:09 – White Paper on Bitcoin – Could Bitoin serve as the role of money 7:43 – Why disruptive innovation is so inefficiently priced 10:04 – How well does the market discount cash flow of disruptive businesses 14:09 – A look at their investing strategies, starting with top-down.  16:10 – How they picked their 5 categories of technological change, starting with foundational     19:42 – Changes in energy 21:53 – Robotics 24:17 – Excitement over deep learning 28:03 – How they express their top-down ideas from the bottom up 36:06 – Mobility as a service as a key area of focus 45:25 – The power of public mistakes 46:39 – What she looks for when hiring 51:14 – her philosophy on building and maintain a portfolio 56:38 – Behind the growth of the company 1:04:01 – Most exciting area for her right now 1:07:52 – Kindest thing anyone has done for Cathie   Learn More For more episodes go to InvestorFieldGuide.com/podcast.  Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub Follow Patrick on twitter at @patrick_oshag

This podcast is sponsored by CFA Institute, the Global Association of Investment Professionals whose mission is to lead the investment profession by promoting the highest standards of ethics, education, and professional excellence for the ultimate benefit of society. CFA Institute serves a global community of investment professionals working to build an investment industry where investors' interests come first, financial markets function at their best, and economies grow. The chartered financial analyst credential is the most respected and recognized investment management designation in the world.

The views expressed in this podcast do not necessarily represent the views of CFA Institute. Hello and welcome, everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com. Patrick O'Shaughnessy is the CEO of O'Shaunacy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaunsi asset management.

This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of O'Shaughnessy asset management may maintain positions and the securities discussed in this podcast. My guest this week is Kathy Wood, the founder of ARC Invest. Kathy and her team believe that disruptive innovation is the key to long-term growth and therefore alpha in public markets. Because their style of investing is entirely contingent on what will happen and change in the future, it is about as different a style as exists from the quantitative approach to investing, which relies on what is currently knowable about stocks and businesses. The future is notoriously hard to predict,

so I'm always interested to hear about investing approaches which try to model and handicap the future and build portfolios against that work. In this conversation, we explore all the most interesting and exciting technology trends at play in the world today, and how those trends may play out for investors. We discussed genome sequencing, blockchain, software 2.0, mobility as a service, automation, and more. We also discuss Kathy's take on building a bridge between the worlds of finance and Silicon Valley, and why starting with a benchmark is anathema to their process. It's hard to deny Kathy's passion and enthusiasm, and I credit her for building a unique firm culture

that emphasizes openness and collaboration. Please enjoy our conversation on investing in innovation. So Kathy, I was pleasantly surprised to run into Chris Berniske on my walk-in today, And when you walked away, he said, you know, you should really think about ARC and Kathy's philosophy as sort of bringing the open source mentality to Wall Street. I love that kernel of an idea and it's a great way to frame the conversation. So maybe you could opine a bit on that philosophy as a jump off point and then we'll get into the whole backstory of ARC and innovation investing. Sure. Well, thank you, Patrick.

I'm really excited to be doing this. So open source, I have been doing nothing but really disruptive innovation in terms of investing for more. than a decade. And as the world has been changing at an accelerated rate, I had this yearning to get at more information than we were exposed to in a traditional asset management firm when I was in one. And yet compliance issues got in the way. And so I wanted to start this company to focus solely on disruptive innovation as opposed to having quantitative research experts risk complete our funds and make them closer to benchmark. So I wanted to get away from that kind of thinking and focus exclusively on disruptive innovation, which I believe is the most inefficiently

priced part of the public equity markets. And then the second thing I wanted to do was add new dimensions to research. Social media, using some of the technologies that had disrupted industries that we had been following for years. So using social media, that includes Twitter, LinkedIn, Facebook, medium, any social media that our analysts felt comfortable on, as well as crowdsourcing, and then allowing thought leaders onto our research intranet. They tend to be professors, venture capitalist, entrepreneurs who, as we exchange knowledge, are interested in pushing the frontiers of knowledge forward. So that was impossible to do in a traditional asset management firm. Compliance was not going to turn itself upside down for one portfolio

team among dozens. And so I felt, okay, this needs to be done and I should do it. Can you describe in a little more detail that platform? You mentioned collaboration. I think is a key part of this and using some of these technologies. Like, what are the nuts and bolts of that? How does that actually work? As far as the social media part of it, we are pushing our research into Twitter, especially Twitter, as it is evolving. The research is evolving, not when it's finished.

And why are we doing that for a couple of reasons? One, we want to live in the communities we're researching, and we feel we have a competitive edge this way. And sure, anyone can watch what these people are saying. but it is in sharing. I really do believe we're the first sharing economy company in the asset management space, certainly as it relates to research. The sharing part is the powerful part. And I'll give you an example of that. A year after we had started this, Chris had become Chris Berniske, had become prolific on Twitter. After in the beginning, I think he was wondering, what the heck is this?

and he was beginning to network intensively in the blockchain space to a point where many people in that space thought ARC was a blockchain company of some sort. Coinbase approached him and said, you know, this was after we had published our first paper, white paper, on Bitcoin. Could Bitcoin serve the role of money? And we did that in collaboration with Art Laffer, who had never put his his name on anything like this before. And Coinbase got hold of it and came back to us through Chris saying, we'd love to write a white paper with you. And I said to Chris, why do they want to do that

with us? I mean, we've barely existed for one year. And he said, well, we are sort of the bridge between that world and the financial services world. And then I really got it. And here I was the one who came up with this idea, but it was going to work so much better than I originally conceived. Because in August of 12, 2012, when I first conceived of this ecosystem, what was Twitter? It was for celebrities, tweens, teens, right? And we had no idea how it would churn out of those and into knowledge workers, particularly in the innovation world. So we were able to write this paper with Coinbase, and we got access to information that nobody else had. It was their information.

They wanted to write this paper. They wanted to help the financial community understand why and how crypto assets could become a completely new asset class. So it was when I had that experience that I said, okay, this is going to work. You mentioned earlier that disruptive innovation might be the least well-efficiently priced thing in public markets. I would love to get to, and I can't wait to get into some of the verticals here, things like mobility as a service, some of the areas that your firm and your research team focuses on.

But first, I'd love to talk about that idea because a lot of the themes, as I was reading the research and preparation for this talk, are really the things of venture capital more typically is what you would think of when you're looking at the sorts of things you're looking to invest in, but via the public markets. So maybe describe why you feel that's true, why disruptive innovation is so inefficiently priced, and what the drivers of that gap closing will be. Hopefully that would be a good thing for innovation investors. Talk me through the thinking there.

Sure. So in the public asset management world, we've seen two big trends. One is passive. So passive simply mimics benchmarks, indexes. Now, what is that? That's very backwards looking. The stocks at the top of these indexes are there because of what has happened, what success they have had in the past.

If there's technologically enabled disruptive innovation evolving, it's probably aiming for them. So that's the first move, passive. The second move that has been very interesting to watch is the search for innovation. What have some of these large asset management companies done? They have been shifting some, I'm not going to say all of their attention, but a lot of their attention, into the pre-IPO markets in a search for innovation. So you've got the move to passive on one end, and you've got the search for innovation increasingly in the pre-IPO space.

What does that do? It leaves what we do inefficiently priced. We would tell you that if you give us a long enough time horizon, now our bottom-up models are five years, our top-down models, which is where I think we really differentiate ourselves, Those can be five, ten, twenty year models. So if you give us a long enough time frame, we will call ourselves a deep value manager. That is how inefficiently priced these long term, you call them themes, but innovation platforms, how undervalued they are right now. One of the biggest topics right now in our world is the outperformance of growth stocks over value stocks over the last, really 10 years now. It's been quite a long run. And this notion that, look, growth can work. If you can find assets, even if they're priced relatively expensively, let's say at a 40PE or 50PE or something,

if they're going to be growing their cash flows at tremendous rates for five or 10 years, there's almost no price you can pay that's a bad price. To your point about being like a long-term deep value investor, but that really requires that the market is doing quite a poor job of discounting the cash flows of these kinds of businesses. And I'd just like to hear more about how you think the market does that. because on the one hand, I certainly understand the markets tend not to get it right at extremes. On the other, a lot of these companies that have themselves been disruptors are now at the top of those market cap weighted indexes. And I would think that the market would adjust to that and start

pricing more growth into some of these verticals that we'll talk about. So how do you think about that, about how the market just does a poor job of digesting that information, despite the fact that growth has done quite well? We've been in a risk-averse market, I think, for a very very, very long time. You may say that growth has outperformed value, but if you looked at what was outperforming, particularly I'll say until nearly the end of 16. Now, I'm sure there are pockets of time where what I'm about to say is not true. But I sensed that a lot of the market, a lot of the investors moving into the market or back into the equity markets, we're holding their noses and saying, okay, I'm looking for yield. I'll move back in. And so for a time there, yeah,

consumer staples, I suppose we're considered gross stocks. We don't consider them gross stocks, but some people do, and they were showing relative growth characteristics for a time. So I look at what happened from 09 to 13 as simply crawling our way out of a disaster. Because then we hit in 13, we hit what many people feared was a triple top. 2000, 2008, 2013. How many times did we hear about the debt deflationary bust that was evolving? Then for the next two and a half years, almost three until the end of 13, we felt it because we started our funds in October of 14. How many risk-off periods did we have one after the other? The month we started, October of 14, when we had our launch party, we were down 7%.

We had only been open for three weeks. You know, that was fun. And then August, September of 15, that was the Chinese devaluation. And some people blame the ETFs for some of the crazy moves that took place there. So that was rocky. And then, of course, January, February of 16, oil prices collapsing. many people thinking China was imploding and yet the Fed was tightening, earnings were disappointing. That was a severe one. Then we had Brexit. And then the last one we had was the election.

Now, I wasn't awake for most of that one. It took place overnight. I was just in Asia. They certainly remembered it. So we went through this period of extreme risk aversion. So maybe this was a growth market, depending on which sectors, as people do, put into those categories. We think the real bull market started in 16, at the end of 16, with that election. And we began to see investor time horizons extend. And I think the reason for that is because they feel their own lives changing, their children's lives changing, and they know they don't have enough exposure to it. So they actually have been using our strategies as a hedge against the possibility that their more index-based strategies are filled with value traps, that our strategies exist for that

reason to capitalize on these new technologies that will create value traps. Let's talk about the strategies themselves, how you think about them, how you construct them. It's not one strategy. There are multiple strategies here at the firm. You mentioned top-down and bottom-up. So maybe we could start with those two ideas. Maybe starting with top-down. It sounds like that is something you really pride yourself on.

So in your 2018 kind of big research ideas presentation, there are things in there that people will recognize crypto assets, mobility as a service. I'd really like to talk about that one. Frictionless transfer of value. All these kind of top-down big thematic ideas. And I think of ARC very much as a thematic expression of some of these ideas. So talk about those two.

How do you balance those? Sure. In terms of actively managed ETFs, we have four. So they all revolve. evolve around the five biggest innovation platforms that we see evolving at the same time. Something we haven't seen since the late 1800s. You had to go back to the late 1800s to see multiple innovation platforms evolving at the same time. So back then, telephone, electricity, and internal combustion engine. From that point, until very recently, we didn't have three or more

evolving at the same time. We maybe had one or two. Today we have five. So we've never been in a more fertile environment for innovation. So the five are DNA or genomic sequencing. The second is automation, which includes robotics and 3D printing. The third is energy storage, so the wholesale shift, we believe, of transportation from the internal combustion engine onto the grid, effectively. Then next generation internet, which features heavily now, artificial intelligence, particularly deep learning, which we think now is software 2.0.

Then finally, blockchain technology. Now, blockchain is built on top of the internet, but we think it's such a big platform evolution here that we wanted to separate it out and focus on it exclusively, analyst by analyst. So how those five categories, why those five? What are the criteria by which you say this is something on par with electricity or I just finished this fascinating book called Creating the 20th Century that describes the key ingredients for technological change as those of energy source. He mentioned some of that. And what the author calls prime movers, so internal combustion engine, you know, changing to something else. And so he draws the constellations kind of back then, kind of like you are today. What are your criteria for that?

So I'm sure there's numbers, you know, six, seven, and eight that didn't quite make that cut. What are the criteria to be one of these like kind of game changing platforms? Okay, so they're foundational. So let's take DNA or genomic sequencing. The innovation that is going to come out of that is mind-blowing. But the first thing we focus on is let's just focus on the foundational part. These foundations submit to something called Wright's Law. Now, if you know Moore's Law in the semiconductor space, you have a decent idea of what Wright's Law is. It is effectively rights law in the semiconductor space. But Wright's law is for every cumulative doubling of units produced, how much do cost decline?

So we are very focused on those technologically enabled cost curves or learning curves. And then the second question we ask ourselves is, okay, for every percentage point decline in costs or price, how much does demand increase? Is this technology ready for prime time? is there going to be a take-up of this technology at this price point? You would have known in the early 2000s that personalized medicine was not ready for prime time. And the way you would have known that is it took almost $3 billion and 13 years of computing power to sequence the first whole human genome. Now, you could have cut that in half to $1.5 billion, and you still weren't.

weren't going to scale very much. There would not be much demand, right? Now, after years of declining 40% per year sequencing costs, we're down to less than a thousand dollars in a few hours of computing power. By 2021, it'll be $100 in a few minutes of computing power. And then it will become a part of our annual physicals, right? Now, why is this so important? Everything follows from this. If we have our DNA sequenced at each physical, what will we learn? We will learn how our genes have mutated from year to year. And mutations are the beginnings of disease. So wouldn't it be nice to catch cancer in stage one instead of, as so often it is today, stage three or stage four? This could not happen without DNA sequencing. Then what comes from that? Oh, well, if a mutation, is like a programming error in the 3 billion lines of code in our DNA, then we can probably figure a way to edit those errors. And so CRISPR gene editing has now, we're just writing a white paper on it. It should be out in the next month or so. But the ramifications are provocative,

but it all starts with that foundational technology. None of this could happen without sequencing. I wonder if we could do those foundational technologies for the other four. Energy storage, battery technology. Elon Musk is pushing the envelope. He's adding silicon to anodes. He's taking cobalt out. There's a little bit left, but his aim is to take it out. That's the real gating factor.

And batteries, which have been, you know, from a cost-decline point of view, you know, very slow relative to other technologies, those cost declines are accelerating to some extent because of what he's doing there in the batteries themselves, but also the battery pack systems that he's put together. Remember in the in the day investors and other auto manufacturers certainly made fun of him for lining the bottom of his cars with cell phone batteries. You know, it was a joke in the beginning. But I think he's going to have the last laugh. The battery technology is that the cost declines are accelerating here so that we believe by 2022, the cost of an electric vehicle, average electric vehicle, will be below that of a Toyota Camry. And when you're dealing

with these immature technologies, what you can then say is the Toyota Camry is mature, its costs are not falling. In fact, they're rising a little bit. And the EVs costs will continue to fall further below. And out of that comes our work on autonomous vehicles and autonomous taxi networks, which we've been working on these models for, well, some of them at Alliance Bernstein, which could not take them with us. So we started all of them here. So they're four years old. And what's really interesting about our research ecosystem, just to get back to that a bit, we have professors taking these models into their classrooms. These are PhDs going for their, you know, they're going for PhD in automotive engineering. And they're taking our models and building on top of them. And

all we ask is that they share their insights with us. Because we know they're not going to build these models in a semester or a year. This is, again, part of the sharing economy. And sharing insights, we get them questioning our assumptions. We get them battle testing effectively our models, which is fantastic for us. So robotics and 3D printing. So I'm curious to hear your take on that. Yes. So interestingly, we're very focused on industrial robots, not consumer robots. Industrial robots have been historically more of an old industrial world technology, especially in the auto sector. I think the auto sector today accounts for 40% of all robots. Well, they're all caged up, but we're now seeing the dawn of collaborative robots.

So these robots are, you know, they can interact with human beings. In fact, human beings can show them how to pick and pack and then, you know, just guide them along and then let them do it themselves. Sam Kores has done all of our work in the industrial robot space, but based on our analysis, we were able to get closer to the mark in terms of industrial robots, the units out there, than the International Robotics Federation, I think. They just came out with the number for 2017, and it was they had predicted 340, we predicted 365, and it's now 1,000. And it's now, so you can see how early.

Now it's at 385. So we were a little below, but we were better than the robotics. And the reason you're going to see this is, and you see this in the financial markets, too, is there's a big difference between linear growth and exponential growth. And if you map that out over time, you will see the compounding effect. I know that BCG, when we did our original study on industrial robots, BCG had the cost decline, they had the slope flattening out, and we did not. They probably were basing their analysis on the auto industry, on a mature industry. You have to be looking at, okay, what's the new,

new, what's going to drive this cost curve down and which industries, to which industries will it apply? So we've been much closer to the mark than some of those consulting companies, again, because of this idea that if they were right, it would still be a growth industry, but somewhere in the 7 to 11% range. If we're right, it's closer to 20%. The power of compounding over the time, you know, the difference between 10 and 20%, you know, compound that over years,

you're massive in terms of the difference in the units that you end up with. The last one on the list is, I know deep learning is a component of it. My understanding, at least, and I'm sure you've done a lot more work on this than we have, is that a lot of this is fueled by some of these same things

we've talked about, but certainly the availability and cheapness of compute power. that deep learning's been around or AI has been around for a long time. And it's only really just recently, kind of like the sequencing of the genome being so much cheaper, that we can start to really apply these methods en masse for affordable costs. So talk a little bit about the excitement from an investing standpoint in that sphere of things. We do believe that deep learning in particular software 2.0 is going to have ramifications for every line item of the income statement. So if you're a

corporation and you're thinking about competitive dynamics, you better start thinking about how you're going to harness deep learning. Now, we have a long way to go yet, but when you see Deep Mind winning in the AlphaGo competition, you begin to think, okay, this is pretty interesting. You see Salesforce.com went from one billion predictions per, I think in the fourth quarter, this is their Einstein, their artificial intelligence, one billion predictions, you know, a lot of this is for marketing and sales, to two billion just in one quarter's time. So if you're in sales and marketing and you are not thinking about this, you're making a terrible mistake. You've got to get onto the leading edge, because if you don't, someone else will. I love the idea that it's going to affect

every line on the income statement for every company that, you know, this kind of honestly simple idea. Like if you just start gathering data and running analytics on that data, like you can improve your decision making, right? Like not revolutionary, but could change a lot of businesses. Absolutely. And what's so interesting about software 2.0, you know, deep learning and so forth is I don't think people understand that what had been holding this back over the years was human beings. And we're, with machine learning, we took, so in the early days of artificial intelligence, because it's been great for science fiction, but it really never did much for us. We didn't feel like it was changing the way the world works dramatically.

And the reason for that is it was completely dependent on human ingenuity. The programmer, human programmer had to anticipate every possibility. Not possible. Just not possible. So then we moved to machine learning, and that's taking some of the human being out and letting data and statistical inference take over to some extent. And then we get to deep learning, and we're really taking most of the human being out, and we're unleashing data and algorithms and iterations to reach some goal that is set by a human being,

hopefully a good and honorable human being. So it's really surfaced for us. You know, Jensen Huang at Nvidia was making what we thought in the beginning, were crazy predictions about deep learning and how it would impact the GPU world, his world. And he was coming, when we were coming up with $6 billion, the way we analyze things, he was coming up with $30 billion and $30 to $60 billion. And within a very short period of time, not even five years versus at the time we were doing this, it was less than a billion.

Now with the software 2.0, we're beginning to understand. understand what is happening here. And it is pretty provocative. And that's why back to every line item of the income statement, you set the goal, you optimize and then let the data and the algorithms optimize to get you there the most efficient way. The fun thing about your perspective is there's this emergent consensus around prospective returns for equity markets. And it's that they're going to be kind of like low, single digits or, you know, unimpressive, below historical averages. And one of the lessons we learned from market history is that consensus is rarely right. And so I always think, okay, what are the two opposites of that? And one is kind of the

scenario you've described for a part of the market. I'm not saying you're saying the whole market's going to do really well. But, you know, maybe the beginning of a long bull market. And then, of course, the other would be, you know, a very negative market. So let's talk about how you take these top down, let's call it, there's beta behind these top down ideas, how you then express these from the bottom up. So if I, if I believe that, you know, pick any one of these, these kind of top down ideas is going to create enormous economic value in the future. I then have to get a second thing, which is like building a portfolio around, around those things. So talk about that bottom up component of what you do and how you take the top down and then express it with actual assets. Sure. A lot of analysts and

portfolio managers screen indexes to get ideas or they use factor analysis. There's something they do with an index. We're not using any index. We honestly don't. We're index. Index, agnostic. So your active share is like 90 something. Yeah, 95, 96, 98. We had 99s at time. So what's our screen? Our screen is our research. Okay, how are we going to build an autonomous vehicle?

Okay, what's that going to take? Well, I can tell you I was floored when Tasha Keeney came into our brainstorming session after she finished on paper what would be an autonomous vehicle. What was going to power that autonomous vehicle? It was going to be. the brains, the central nervous system of another, going to be a GPU. I said, what? I said, the market doesn't know that.

The market has no clue. This was 2014. InVIDIA was nothing more than a PC proxy. And then James, in the same brainstorming session, and I would encourage you to join us anytime you'd like, and he said, well, okay, that makes sense because this is an AI or deep learning problem. And I said, and GPs do that best. I said, James, the market doesn't know that. I know you worked at Nvidia for nine years and you know that, but I didn't really know that. I didn't know that. I hear

everybody talking about it and artificially intended. He said, no, they have 80% of the training market. You can't do it without a, in fact, you can't do it any other way. Maybe the inference market is up for grabs, but the training market is GPUs. And I said, Okay, nobody knows about that. And then, wouldn't you know? Market gets it right eventually. No, no, no, what happened then in the same meeting, Manisha, and this is why we do these brainstorms, Manisha said, you know, it's funny. At the end of the research papers that I read on genomics, invariably, invidia is mentioned. I always wondered that, why. And James said,

well, yeah, because you're going to need artificial intelligence to deep learning to understand pathways and everything. And I, as the portfolio manager, I was sitting there saying, okay, this has to be one of the biggest inefficiencies I've ever seen in the public markets. This was 2014. So, Invidio was somewhere in the $6 to $10 billion range. Today it's, what, $160, $170 billion. not so long ago. That's how big the inefficiency was then. Now, we would say it's still inefficiently priced because we've built our models out for autonomous vehicles. That's a huge, that's one of their killer apps. We've built it out there for data centers, so artificial intelligence, deep learning, virtual reality, augmented reality. And yeah, throw in genomics and seismic and the oil industry. There's just, you know, there are so many applications.

So we still think that investors don't have it right. And we are not even giving Jensen Wong full credit for his estimates out there. So what I've just illustrated is the screen for our portfolios is not an index. It's our research, as it should be in terms of the kind of thing we're doing. We want the stocks to come to us, you know, and there's always a cornerstone stock. And then you watch, you look for the ecosystem around the cornerstone. So in DNA sequencing, the cornerstone stock is Illumina. It has 90 to 95% share of all the base pairs of DNA sequenced in the world today, including China now. China gave up, well, it seems like they'll never really give up, but they've had to reorder from Illumina because their efforts after buying complete genomics did not work.

They do not have their own DNA sequencer. And yet they want to be number one. in the world in terms of that genomics revolution. So 90 to 95% share, Illumina, good starting point. Now, what's a great story there right now is most analysts think their growth rate's going to be 15%. They just got it back up from, you know, high single digits, low double digits, back to 15%. New product cycle, Nova Seek and everything. If we're right on what's going to happen with this massive cost curve decline that they're experiencing and our estimates of the price elasticity of demand based on modeling, which takes recent experience into account, the 40% cost decline

will result in a 200% per year unit increase in whole human genomes sequenced. So to give you a sense of the escalation here, we're talking about going, from 1.5 million whole human genome sequence last year to 170 million in the year 2021. That's more than 100 fold. Now that's units, but big price declines. If we're right, while most analysts, I think, the consensus view has revenue growth in the 15, maybe they'll be a little out there, 17%. We could see real revenue growth,

accelerating past 30%. We don't even have to go past 30% given where the consensus view is right now. But we can get there. And the way we also get there is I just gave you whole human genomes. We haven't even talked about plants, livestock, viruses, bacteria, the microbiome, cancer tumors. Cancer tumors have to be sequenced and then resequenced and resequenced. So what this top down does for us is it gives us perspective. and because the rest of the world is so much more short-term in its focus. And I think that's a function of two crashes in the last 15 to 20 years, tech and telecom, and then 0809.

So, you know, this idea of what are you going to do for me today became the prevalent view and the prevalent way of looking at stocks. We just think that's wrong, especially given what we do. And I'll just add one more thing. many, whether they're consultants or portfolio managers, investors generally, they consider what we do to be, we're focused on niche areas of the market. That's not how we're thinking about it. This is how the world is going to work. So to give you a sense of the conviction level I have in our research and in these ideas, all of my IRA is in our funds or in Bitcoin.

One of the lessons that you've highlighted a few times is that markets just tend to do a poor job of extrapolating exponentials. They're not good at pricing in exponential growth over a multi-year period. The one area that we kind of brushed over, but I really stood out in the deck was this idea of mobility as a service. So we've touched on aspects of this, I understand. But there were some kind of crazy ideas in there, things like air taxis and drastic reduction in the cost of moving things. So not just people, but automated trucks and all these other things. Any other ideas or deeper thoughts on mobility as a service is a key area for you? Sure. And before I do that, I'll just on this notion of exponential growth, it's understandable. I do not want to be, I am not denigrating. Market's not dumb. In fact, I think the market is very wise over time. But, you know, there have been false starts in some of these exponential growth opportunities. I gave you one at the very beginning, which is we were not ready. for prime time when it came to personalized medicine in the early 2000s. And so there's been, okay, this

exponential growth thing is a bit pie in the sky. It's not pie in the sky the way we're doing it. We're doing it, as I mentioned, centering our research on rights law and figuring out the price, elasticity of demand. So we figure out, are we ready for prime time? So mobility as a service. It's such a fascinating. It's going to happen before we know it. And in fact, it's happening. No, Google's out there with its cars in Arizona. Mobility as a service is going to happen, and it always comes down to economics. First thing that's going to happen is what I mentioned earlier, the price of an autonomous vehicle dropping below that of a Toyota Camry.

And then ride sharing has given us a preview of what's going to happen as we go autonomous. First, we know it's going to be in demand. ride sharing is in demand, even though it's not, it's priced not much less than a taxi. It's the convenience that's really the convenience and, you know, multitasking we can do while we're in transit. How much money could we save with an autonomous vehicle compared to what we're paying for our personal cars now? So we have prepaid for transportation when we buy a car. And we only use our car 5% of the time. People will wonder, you know. How do we live that way?

Yeah, how did we live that way? And Brett Winton, our director of research, says that he believes we will consider cars, human-driven cars, weapons of mass destruction in about 10, 15 years' time. He has two little kids, and he's very, very happy. Hopefully they won't be driving. They will not be driving. He said, yes, but when you think about it, think about when you started driving, when I started driving. I mean, I should have been driving. And I was in Southern California, man, you turned 16, you were out because this was freedom. So, and now in Southern California, the number of new licenses being granted to 16-year-olds is falling. It's not as cool to have your own car. It's a waste of money. So anyway, we learned in doing this research, and I was surprised that we right now pay 70 cents per mile for our transportation point to point. That number has, has not changed if you do, if you inflation adjust since cars first moved off the assembly line.

So for us, the way we look at that, that's no progress. That's no progress. So that's 70 cents. During the horse and buggy years, it was $1.70, right? So that was real progress. Look at what that progress created, right? Yes, yes. Now we're going to cut that 70 cents in half to 35 cents.

And that assumes there are going to be human beings, but they're going to be like air traffic controllers. So as artificial intelligence gets better, we'll need fewer air traffic controllers, so that 35 cents will continue to go down. If you cut the cost of something by half, demand explodes. Yeah, demand explodes. The other thing that's interesting about mobility as a service is auto sales are going to fall. Why? Well, we use our cars 5% of the time. An autonomous taxi fleet, Those fleet owners will have an incentive to drive capacity utilization up to 70, 80 percent. So, you know, we're just not going to need as many cars. We're also not going to need that many parking spaces.

Right now, I think we have five parking places. I think this is in urban areas for every car owned. We will only need one parking place for every car owned. So think about how real estate will change as well. So these are the sorts of things we're thinking about. We also think because electric vehicles are four times more efficient than gas-powered vehicles, that oil demand will peak in the next two or three years, if we're right. Now, you see oil prices escalating here.

I think the peak in oil prices is when that Aramco deal is done. And it will be about a year or two after that. So they're going to try and do it in 19 that oil prices peak. So we see, because of this collapse in the cost of transportation, we see vehicle miles traveled increasing two to three times in urban areas. And that forced us into this air taxi analysis. Because I remember sitting in a brainstorm. And you've heard me say brainstorm a few times here. I got to get involved in these things. You have to. You have to. Because it's like, well, wait a minute. Tasha brings in that two to three

times. I said, there is no way. I'm going to spend three hours getting to the airport. okay, we've got to have flying cars. And so at first, it was a little bit of a lark. Well, let's just see if battery technology could advance. Because we know helicopters are not the answer, way too expensive. But could battery technology get us to a point where it would become reasonable to fly to the airport? And we did the work, this is Sam Khorst's work. And we learned that, heck, in the year 2021, 22,

do, I could pay what I'm paying now to go in a taxi, to go in an air taxi, about $75. And at that same time, it would cost maybe $7 to $10 to go in an autonomous taxi. So the relevant comparison to that 35 cents per mile that I mentioned before, so dropping from 70 cents to 35 cents, in a taxi, it's $3.50. So it will be one-tenth the cost of a taxi. So instead of paying $75 to go to JFK with a tip as we do, we'll be down to $7 to $10. And so you understand why it's going to be so crowded. So that's what got us to our air taxi. We did the freight drone work in 2015.

And this was Tasha doing that work. And we learned back then that Amazon, who was the most advanced in drone technology, that Amazon would be able to transport a five-pound package over 10 miles. I think that's right. Yes, a 5-pound package over 10 miles for $1 profitably based on, again, pulling apart a drone, trying to figure out its components, what it would all cost. And we were surprised.

Silicon Valley came back to us. We got so much incoming DMing, you know, over Twitter and emails saying, how did you do that? Where'd you get that? And where'd you get those numbers? And it was then that I realized, ooh, this is really interesting. We are doing something, the kind of research we're doing is, and I've corroborated this since, is the kind of research I thought venture capital was doing, the Silicon Valley was doing. But it's not. It's not. and was like, wow, we could really serve as a bridge. You know, Silicon Valley and financial services have, you know.

Very different ethos. Yeah, very different ethos. You're right. And we have to live with each other. We have to get along. We feed off of each other. And yet very different dynamics. And as you say, ethos, I was thinking, wouldn't it be fantastic if we could be part of

the bridge between those two worlds? You know, it would be a great service, I think. But also it would be a valuable role. And so we've even started thinking about someone has come to us, you know, the kind of work we could do in a venture world and change the dynamics there a bit. Because I'm sure there are a lot of companies, small startups, that don't get into that club,

and it is a club, as I'm learning more and more. They don't get into it. They're left out, but maybe it was a promising technology. That's the only reason I would want to do it is to make that world more efficient in the way that we're trying to make the public world more efficient. I wouldn't want to be a me-to. One of the things I've found, certainly in the Twitter world,

where I'm pretty active, is that the fastest way to learn is to be even just slightly wrong in public. Yes, yes. You get corrected very quickly. Perfect. So that is exactly why we wanted to do this. We need to battle test our assumptions because we're dealing with exponential growth. If we make an incorrect assumption and then carry it out,

we're going to make an exponential mistake, which is going to be really wrong. We'd have a crowdsourcing part of our ecosystem as well, where because we put our research up on our website, our conclusions, not our models, we are quite happy to have blog aggregators, like Seeking Alpha, take our research, slap it on their side if they think it's going to drive traffic, and why are we happy? We don't have to do anything. We can just watch the bulls and bears insult one another over our assumptions. And we can also find out okay, have we thought about every contingency or everything that they are feeding to us here? And that will, likely if we haven't, it will become a part of a brainstorm.

And it's much better for us to catch our errors early and then correct them and be told in no uncertain terms, how wrong we are. You know, that's fine. You've mentioned your team quite a number of times and a number of different members of the team as research leads in different parts of what you're doing. I would love to hear a bit about your thinking, philosophy, evolution when it comes to who you want to hire, what you look for in people doing this kind of work. In asset management, obviously, you want the best research people possible. What does that mean to you? What it means to me, and again, I'm going to say this, but very respectfully, it's unlikely we'll hire anyone from the traditional financial services world, traditional asset management world. it's not because they aren't smart.

They are so smart, but they have been trained in a certain way that is not who we are at all. And there's a place for what they do. And there's a lot of that going on right now, especially the bottom up. And we are as bottom up stock research driven. We listen to all the earnings calls, but we don't have the same time horizon. That's the difference. What we'd like for our analysts is domain expertise. So James Wang, I mentioned earlier, he was at Nvidia for nine years.

So that is the artificial intelligence chip company. And he's highly networked in that world. And I think what we've done is given him a platform. He's really thriving on it. He's very active on Twitter now. Took a while for everyone to rev, but now they see the power of this. So now he's being invited to do conference. around the world on artificial intelligence.

So that's part of what the sharing thing. They know he knows his stuff. And by going to these conferences, he's networking with people in a way that most financial analysts can't. Manisha Sammy, she was in Stanford University's biology research labs doing research for eight years, four of them in high school, four in college. She was doing potent stem cell research.

She was doing CRISPR experiments. Now, most of the well-respected and highly experienced health care analysts in the financial space, they have not been doing those experiments. They have been watching drugs go through phase one, phase two, phase three. They know exactly what to do when this signaling takes place and that signaling takes place. But what we have going for us here is, I'll just give you an example. It kind of says it all for me. So kite pharmaceuticals in CAR-T technology, which is another technology that Manisha studied at Stanford,

CAR-T technology unleashes a person's own immune system against cancer. Our immune system doesn't recognize cancer as a menace, right? So we have to teach it. Kite pharmaceuticals was focused on aggressive non-Hodgkin's lymphoma. Now, the only patients allowed into the trial were those. who were on their deathbeds. They had failed three to four other lines of therapy, and there was nothing else the doctors could do. The doctor basically was saying, I'm sorry. So, Hail Mary Pass, they're in this trial, and it's a phase three trial. So it had gone through all the safety trials

and all of that passed. Final. So phase three trial, there's a death. And the stock goes down 30, 40 percent in a matter of days. And I said to Manisha, I said, Manisha, doesn't it surprise you that more patients didn't die, that there was just that one patient that we've heard of so far? She said, that's right. She said, there's probably a pretty good remission rate here. And so we bought the living daylights out of kite. It became one of the largest position, certainly in genome, but even in the overall disruptive innovation portfolio. And sure enough, they resumed the resume the trial, and when we got the trial results, I don't remember the exact numbers, but they were around 50% complete remission or something like that. There's something in

response and then complete remission. I'm probably too high on the complete remission. But what we've found out since then is those who survived up to that point, the percentage surviving from there continued to increase. So everyone was selling that stock because they knew what to do with a phase three death, that's a sell. This thing is done. It's cooked. It's not going to make it out to market. That's not what happened. We've talked a ton about really the fundamental side of the investing equation. So if there are really only probably two ways to earn a big edge, it would be understand the fundamentals better than the market does or understand what's in the price better. We haven't really talked about price that you have to pay to buy some of these stocks. I don't know the

stocks, you know, one by one, but if I had to wager a guess, they're true. at multiples that are considerably higher than the market in aggregate. And that's all relative. If they're going to grow at 30% a year or something, like those multiples can pretty quickly be justified. But getting that right has historically been hard. A lot of the best winners have emerged from that expensive category, but most of the worst losers also emerge from that category. So that raises the question of risk and price. And as it pertains to your thinking around selection and building a portfolio, and I would love your take on those ideas. Sure. So it comes back to time horizon again. So if you looked at our portfolio, we've had value managers and growth managers say to us, value managers in particular, we would never buy one stock in this portfolio. But we know they're going to be a problem for some of those stocks are going to be a problem for our portfolio. So we'll put you in as a hedge, you know, kind of holding their nose, right? So very high multiples in the short term. And one of the reasons for that, we bless.

We want them to be spending aggressively to capitalize on the enormous opportunities ahead. This is going to be like Amazon in the early 2000s. When I remember in 2002, I wasn't long after I joined my last firm and, you know, we're in the middle of the crash. And I put Amazon in the portfolio. Literally, you know, it was like they were stunned. What are you doing? Like it's never going to earn any money, you know, internet's figment of Wall Street's imagination, you know, and so forth.

We learned a very important lesson from Amazon. If you can tell me that the revenue growth of a company is going to be sustained at 20 to 25% plus and accelerate in the later years as we enter the sweet spot of the S curve, which tends to start happening when market share reaches 5 to 10%. if you can tell me that, I can, with my cash flows out there, especially as we see Amazon Web Services layering in, and now Amazon marketing services layering in, which is advertising. It's going to be an interesting, interesting dynamic in the next few years. And then logistics as a servant, we do believe that Amazon will be a triple-digit return

on invested capital company out there at some point. That's what Jeff Bezos said. I think he said it as early as the IPO, which was 96 or whatever it was. So we're not looking at current multiples, which is not. And anyone who does will not want to be a part of our strategy. But what we're doing in terms of our scoring system, so we do our top down, and then we do our bottom up, and then on top we have a scoring system. The score zero to 10, anything that drops below five just is out of the portfolios.

but some of this is subjective or qualitative, one of them being company management culture. You need the visionary. You need the drive. You need change fast, agility. One of them is moat, which it was interesting to hear Tesla or Elon Musk in his last, very colorful conference call, say that moats are lame and then proceeds to say, the key is innovating faster than anyone else. That is his moat. Right.

certainly on the battery side. So we have the scoring system, and one of those is valuation. So we believe every stock in our portfolio right now will over the next five years. And we say to investors, if you can't give us five years, don't, because we'll make you too nervous, and we don't want to do that. But if you can, then we believe every stock in the portfolio. We believe today, based on our research, will deliver a minimum. minimum of 15% compound annual rate of return over the five years, not every year. So that's a doubling every five years. Why do we feel so confident in that? It's because revenue growth is north of the 25% and will be sustained. So there's a lot of room there for investing and risk off markets

over a five-year period. So that's one of our scores. And it changes the most of any. Why? Because of market movement. So a market can take one of our stocks. If we get one of the big broker stock, if we get Goldman upgrading a stock, having been negative on it, and I don't know if they ever upgraded Red Hat, but that one or something, because they obviously were partial to Oracle. But if we ever get that kind of dynamic and a stock is up 30 percent, we'll be taking profits. And the same on the other side. You know, we get Oracle, whether it was Red Hat in the day or Salesforce.com saying, we're going to wipe them off the face of the earth. You get these horrible moves down, and that's where we'll pick up stocks. So we're not momentum

driven at all. We have talked pretty much exclusively about big ideas and investing. We haven't touched on the business itself, and I'm always, since I'm in this world fascinated by the businesses behind the strategies. I was joking with Chris at the front that, you know, it wasn't that many years ago that he joined you to do a number of things on the research side, and you were at like $300 million dollars of assets. You know, we're sitting here today, you're at six point something billion in assets very quickly. Talk a little bit about what is behind that growth. This is a stark counter example to some of the trends you mentioned earlier, passive, low cost, all of these things. So it's sort of an example that disproves what's been the common trends. So talk about why you

think that's happened. Is it something that is marketing led, sales led? Is it because of this kind of more open source, interconnected research methodology. Who are the people that are buying your strategies and how do they hear about you? Okay. Well, we were very fortunate in the very early day to have a foundation offered to seat us, heard about the idea and just want to, and they're still with us, which is fantastic. So the first four ETFs, they seeded. So owe them.

And then a second milestone was a state pension fund had followed me from, my previous firm to here. They gave us $200 million when we had $19 million under management. So that was a second huge breakthrough for us. That was in March of 16. Right. So we started October 14, March of 16. And we had talk about, you know, having to be the cheerleader around here. We had four months plateaued at that $20 million level. And, you know, a lot of the, you'll see a lot of young people here. They wanted to be a part of this because the way I had described it was, actually I can give you Chris as a great example of the kind of questions or resistance until they really

understood how serious I was about this. So Chris, I met him when he was at Stanford, he was doing a big data project at the time my son was going around touring schools. And I said, oh, he was doing it in the master's program or for the master's professors in the Earth System School. And I said, oh, big data, yeah, we're all over that. This was in 2014. And I said, you know, you should think about joining us when you graduate. And he said, well, there are three reasons I'd never do that.

And he said, the first is you're in New York City. And he's from Hawaii and California, right? And when I met him, he was barefoot with a skateboard and bleached blonde long hair, right? The second is I'd have to sit in front of a computer all day. And the third is you work in the financial services business, and that's the dark side of the world. And when he said that, I said to him, if you really believe that, then you must come and work at Arc. If you want to change the way the world works, you come. He didn't. I thought we were done with the conversation at that. Eight months after that conversation, he called me up and he said, do you still have a job for me? And I said, I knew he was a talent. I knew it was a great talent. And so I said, yes, yes, yes. And are you sure you want to come to New York City? Yep. I've been reading the research. I think this is what I want to do. And it was a great move for him. It was a great move for us, too. You know, he was instrumental in, in

pushing us on the research side. He and, of course, Brett Winton, our director of research, who's phenomenal as well. Yeah, so those are the early days of raising assets, right? And then, and then you've had this acceleration. Yeah. What happened was I funded this company for two and a half years by myself. Thanks, be to God, I was able to do that, right? Over, you know, I've been around the track a few times, so I've had a number of years to save. So that was part of it. So I knew that we could not build distribution in-house. I don't think it's healthy to have captive distribution. That's just my opinion. I feel we're not distribution. That's not our core competency. We must focus on research and investing. We know who we are. So I was looking for

strategic partners for district. But we went to the altar a few times. And then I just pulled out, I said, no, these people do not understand what we do. We need someone who really believes in what we're doing. American Beacon, which is now known as Resolute Investment Management, came along, and Gene Needles has been in distribution for a long time. And he understood both active, because that's been his primary focus over the years. But he was at Invesco when they bought Power Shares or Pro Shares, one of those two. So he understood passive and ETF. Now, we were a hybrid, active NETF.

And he convinced me that he knew what to do. And we had already struck a deal with just a distribution deal with NICO asset management. No, that was right afterwards. We struck a deal with NICO. But NICO took a strategic interest in us the following year. So they each have a minority interest in ARC. Those distribution deals were critical to getting us going. I think in the beginning when I thought I had made a terrible mistake starting with

ETFs as the wrapper. See, I was trying to go, as Tom Stout, our C.O. would say, to where the puck was going to be. And I thought, gosh, ETF's perfect for active management. It's better for the end consumer. It's cheaper, more transparent, more liquid, more tax-efficient. Why isn't the whole world going this way? And so I said, let's start there and we'll do active. No one else wants to do active because they don't want to disclose holding us at the end of every day. Let's do it. Because I'm not afraid to do that. We're not momentum driven. So I thought that I was being so smart about it all. For the first two years, it looked really dumb because I was talking to people in the ETF industry. They didn't even know what active was. They had never even considered it. And I had never considered that,

they would never consider active. So it was really hard. We couldn't get the ETF strategist to put us in their models. No way. No way. They didn't understand us. So I thought I had made a bad mistake. And as it turns out, one of the reasons for the strategic partnership is American Beacon, which is a mutual fund platform, wanted an ETF platform. So that worked out. But we have really differentiated ourselves in being active and, you know, doing the right thing. for the end customer. Now, there are some who cannot use ETS. For example, define contribution. You have to use mutual funds because of the fractional share issue. So there's a place for every wrapper. But, you know, if there's a choice, I think most investors would go to an ETF. So they've been critical. And that's,

that really is how we've scaled. What is the most exciting area for you right now? We've talked about kind of all the most exciting things happening in the world. I know that stuff changes and evolves very quickly. What is the most top of mind kind of most interesting thing, maybe at one of the brainstorms or in a report your team has put out that you've seen lately? I do think it's this idea that I don't think, in fact, I know that the CRISPR stocks are not being valued correctly. Because unlike during the tech and telecom bubble, for example, when companies were patenting genes or they were filing applications to patent genes, the UK court, basically squash that. And it was, I think that's one of the reasons that bubble popped. It wasn't

tech, pure tech. It started with biotech. And, you know, if a company filed an application for a patent, it would go up double, triple, quadruple. Today, we have three companies that have the premier patents in the CRISPR space, CRISPR CAS 9, which is the first flavor and enzyme. And you've got, what do you have asset managers or portfolio managers saying? They're saying, oh, they're all fighting over patents. No, can't get near those. Or they're saying, nope, too small cap. You know, they're one to three billion dollars.

Too small. Or they're saying, oh, they haven't entered human trials yet. They're just entering human. We'll wait until we see that. So, you know, we know we're not in a bubble. We've got no chasing of these stocks. In fact, we have had lots of, shall I say, opportunities to buy these with any little controversy. That's another thing from a psychological point of view that we see in terms of trying to understand,

are we in a bubble or not? We are not. Let me tell you, we're not. These companies hold the cures for disease. So by our estimates, there are 1 in 100 babies is born with a monogenic disease. That means a disease caused by. the mutation in one gene. Only 5% of those have treatments. And I think the number is something like 30% of them will not make it to their fifth birthday. So we are going into human trials this year.

I think we're just entering for pediatric blindness. We are going to know in a few months whether this is a cure. Now think about that. Now human trials, yes, could be very different. It works in mice, works in non-human primates. It works in China. They've been doing human trials since 2015. Now, we don't know what doesn't work. That's why we need to do the human trials in the U.S. But if we have this cure, I mean, these stocks are going, they'll go crazy. I think. We're not in a bubble, so maybe I'm wrong. And people will start worrying about something else. But it's been really fascinating for me to learn about this and to say, wow. And do you know monogenic diseases are only 2% of all diseases? And yet this is one in 100

babies born. Our estimate is if we can just cure the monogenic diseases, that's a 75 billion dollar revenue opportunity per year. And it's a $2 trillion windfall if we were to correct the monogenic diseases in people that are walking around with them today. And I've just told you that's 2%. And then there's a whole new frontier beyond that in terms of polygenic diseases. So I think for me, that's the most exciting that I just don't think people understand. Yeah, I listened to the CRISPR book on audiobook recently, and it's about the most mind-blowing thing you can imagine. Highly recommend people do a little research on that. Well, this has been, you know, an absolute blast. I love technology. I love talking about investing in technology.

It's very, very different from what we do, which is always refreshing and interesting. I have one final question, which is my closing question for every single guest, and I've got to start compiling in these somewhere because they're always interesting. The question is for the kindest thing that anyone's ever done for you.

There are many kind things. I think, I mean, this is the sharing, you know, when kindness begets kindness. So I try to be a kind person myself. I remember, this was actually the beginning of my journey to end up, starting this firm. In 2006, I was just, I was so depressed because I was in a situation where, you know, because of the tech and telecom bust, there had been already a shift towards benchmark

style investing. And so that's how we were judged, even though I could say till I was blue in the face, give me five years, give me whatever, you know, trough to trough, give me something. And I remember that you're underperforming and you know I should never I should never ever have let myself get this way because I consider this today you know worshiping an idol the almighty benchmark which which I should never ever have fallen into that trap or let anyone make me feel any which way about it but I did the market was up a I don't know we underperformed by 1100 basis points so I think the market was up 16 and we were up only five something like that. Well, that never happened in my strategy. You know, we were, you know, we've got

beta and alpha, you know, going for us usually in a year like that. And instead, it was a commodity cycle. It was a, and I just remembered being very depressed. And I don't know if you've ever heard of him. You should interview him if, if you haven't. And I'm speaking about kindness from a professional point of view. That's how I'm limiting it to this conversation. But he gave me a plaque with a saying on it and it's in my house today. And it said something like, it's from an ancient philosopher and it was the most beautiful thing in the world was seeing the mighty, you know, the brilliance and whatever other words he used as a person who's, I'm going to rephrase it, who is under the gun and suffering

how they strive to overcome that. And obviously, this is from ancient times, everybody's gone through this. I happen to create an idol that I should never have worship, so I'm not happy that I felt that way. But it was very, he believed in what we were doing so much that he wanted to encourage me and tell me, look, fight on, keep going. You're going to win. Fantastic. Yeah. Who is that?

His name is Kiril Sokoloff. Oh, sure. 13D research. Right. And I found him. in 2003 because I inherited a bunch of portfolios from a portfolio manager who was retiring, and I inherited therefore the research. And I found this research and I said, wow, this is fantastic.

We are definitely simpatico, similar DNA. And we are now partnering. We've just been talking about doing some high-level, some dinners around the country, if not world, to expose some of these ideas to people. who should be investing in them. Well, Kathy, thank you again for all the knowledge dump today. It's been a ton of fun. Thanks for your time. Thank you, Patrick. This has been fun. Thank you very much. Hey, everyone. Patrick here again. To find more episodes of Investor like the best, go to investorfieldguide.com forward slash podcast. If you're a book lover, you can also sign up for my book club at investorfield

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