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Welcome back to The Bull, your personal finance podcast. Ladies and gentlemen, today just two simple words could be enough to introduce the episode you're about to hear, Cathie Wood. Cathie is probably the most famous woman in the entire global financial landscape and without a doubt the manager behind the most iconic active ETF ever created. Like all extraordinary figures, her work has often been polarizing. In 2020, she became a living legend when her flagship fund outperformed virtually every other equity fund, up more than 150% in just 12 months, thanks to visionary calls on companies like Tesla, Zoom, Coinbase, and many others that soared at exponential rates during that remarkable period. Then came a long winter driven by rising interest rates which sent the fund down over 80% much like the NASDAQ during the dotcom bubble. But starting in 2023, a powerful recovery began almost plus 200% since then and plus 50% year to date, bringing the fund's overall performance since inception to nearly 16% annualized. Since 2024, ARK's ETFs have also been available in UCITS versions listed on the main European exchanges. With Cathie, we talked about the core principle behind her investment, technology-enabled disruptive innovation, as well as the key investment themes of the future, her unconventional mindset, her views on Bitcoin, and the approach she believes investors should adopt when looking at these sectors. But without further ado, here's my visionary conversation with the great Cathie Wood. Enjoy it. Cathie, welcome to my podcast, The Bull.
Oh, I'm happy to be here and I love the title of your podcast.
Oh, thank you so much. It is supposed to be a somewhat optimistic one.
Yes, very very futuristic, I would say.
Yeah, that would be the idea. It all started as less than a side hustle. Uh then it has suddenly become my full-time job. It's a serendipitous story, let's say that.
Oh, that's wonderful. That's wonderful, Cathie. I'm thrilled to have you here. It's an amazing opportunity for me to talk to you. You were mentioned several times in my podcast because it's not an overstatement to claim that you are one, if not even the most famous and influential woman in finance all around the world. So, I'm very, very honored to have you.
I'm thrilled to be here. I have just taken a trip throughout Europe. I didn't hit Italy this time, but we do regularly because I think Italy is very thematic in its investing style and I think the investors in Italy are very tech-savvy. So, it's always a pleasure to speak. Please come visit us next time you travel to Europe.
Of course. Yes.
Okay. As I said earlier, you are very well known among my audience. Therefore, there's no need for an introduction because your name truly speaks for itself. But please walk us through ARK's history. Why did you decide back in 2014 to found ARK and what its mission, vision, and strategic view are?
Sure. So in 2014, I founded ARK for two reasons. One was to focus exclusively on technologically enabled disruptive innovation and then the second one was to open source our research. So why only technologically enabled disruptive innovation? Well, I felt we were going to fulfill an unmet need. That is why anyone should start a business only if there is an unmet need. And what we saw happening really starting in the early 2000s during the tech and telecom bust and then even more so after 08-09, we saw a move in traditional financial markets asset management towards passive and even within active investments towards benchmark sensitivity. So looking at the S&P 500, the NASDAQ, the MSCI World, looking at the weight and say, I'll have a little bit more of this, a little bit less of that, letting the index really govern the investing. And I felt it was really important given all of the technologies evolving. So the five major platforms: robotics, energy storage, AI, blockchain technology, and in the life science space, multiomics sequencing. They involve 15 different technologies. The technologies are converging and I didn't think enough research was being done on these opportunities and therefore there was not enough investment in these opportunities and I felt that we could fulfill an unmet need but I also think from a competitive point of view countries' competitive standing in the world that we needed to allocate more capital to these areas in the public equity markets. Now, since then, we've started a venture fund. We started 3 years ago, and we see that kind of investing take place in the private world. But as the physical and the digital worlds meet, we're going to need the public markets to scale these opportunities.
Yeah, that's fascinating. Thank you. And how do you define technology-enabled disruptive innovation when it comes to picking a company and deciding okay that fits our vision?
Sure. Well, first of all, an innovation platform has to meet three criteria. One, it has to be technologically enabled, which means it follows a learning curve. And those learning curves are expressed through cost declines associated with efficiencies. And we've centered our research around something called Wright's law. So Wright's law is a relative of Moore's law, which basically said twice the power at the same cost every 18 months to 2 years. Okay. Moore's law is a function of time every 18 months to 2 years. The semiconductor world followed that cadence beautifully for decades. But now it has hit up against the laws of physics and Moore's law is not working in the semiconductor world anymore. What is working is what we use for every technology. It's called Wright's law. It is a relative of Moore's law. It is not a function of time. It is a function of units. And I'll just quickly give you what the law says. For every cumulative doubling in the number of units produced by a given technology, cost declines by a consistent percentage rate. So for every cumulative doubling 1 to 2, 2 to 4, 4 to 8, cost declines by a consistent percentage rate. So we need technologically enabled companies that are going to be following Wright's law. And why do we need that? Costs come down, demand goes up as prices follow costs down. And then the second characteristic is that these technologies are going to cut across economic sectors. And so even in the multiomics sequencing space in life sciences, many people think that's just about human health. No, it's not. It's about agriculture, crops, animals, and so forth. Right? So scales across sectors and if we're right on these technologies they become very big opportunities and really in the early days we're looking for a low base. The technology is just emerging and certainly DNA sequencing is at a very low base just for example. And then the third characteristic is that these innovation platforms serve as launching pads for more innovation. And an easy example there is, okay, now we have DNA sequencing. It used to cost $2.7 billion in the early 2000s to sequence one person's genome. Today, it costs $200. So you can see how deflationary the trend is and therefore how explosive this is going to be. Why? Because if we can detect mutations which are programming errors in our genome and we get more and more of them as we age. Well, now there's a new technology, CRISPR gene editing, that can reprogram the genome and cure disease. And that's not futuristic. That's happening now with sickle cell disease and beta-thalassemia, a company called CRISPR Therapeutics. So those are the three characteristics that really as we're doing our research and just asking questions, what is an autonomous vehicle? In 2014 when we started the company, what is that? Well, we learned Tasha Keiny went out and consulted with academia, with futurists, with people who were dreaming about this new world and learned very early on that the brains or this central nervous system of an autonomous vehicle was going to be a GPU. At a time when NVIDIA was being dismissed as nothing more than a PC gaming chip company and PC sales were dropping at a double-digit rate. So we got into Nvidia when it was less than $10 billion in market cap and now it's trillions of dollars.
Thank you so much for the definition and for this perspective built upon the more extended to different fields and across industries.
Right. Right. And it's very pervasive and very deflationary and I think that's going to be the big surprise in the next few years. How these deflationary trends in technology are going to permeate the global economy and really cause surprising unit growth and surprisingly low inflation.
Yeah. Very often this year you've mentioned those five big investment themes you're really passionate about. Could you please dive a little deeper into them?
Sure. So and this would be the five platforms just give you a little insight into each of them. Well, I mentioned the five platforms. What's interesting is the biggest ideas from our point of view are those that are harnessing more than one technology and are taking advantage of the convergence among technologies. So Tesla we regularly feature as the poster child here. Elon Musk in his master plans if you go back and read them he understood that technologies were going to converge and he also understood that one of the most important competitive advantages would be in data proprietary data so think about well Tesla certainly has proprietary data on all the roads around the world but he was thinking much more broadly. SpaceX has data on space that nobody else has. Neuralink has data on the human brain that nobody else has. X has data from well, you know, all kinds of people. And it is surprising to us in 2014 when X was called Twitter. We never thought Twitter would be our most important social network, but it has become that because so many innovators are talking. There are a lot of others. But back in 2014, it was tween, teens, and celebrities and not so many innovators. And then we started using it and we saw oh wait a minute there are a lot of people talking about innovation. So he has all of that data as well. Tesla is the convergence of robotics, energy storage and AI. Elon understood very early on that autonomous travel is where we were going. So autonomous vehicles are robots and Tesla has 7 million robots roaming around the world right now. I have two, a Y and a 3. Energy storage because costs associated with electric drivetrains are continuing to fall whereas gas powered vehicles the internal combustion engine is very mature and those costs are not falling anymore. We believe that robotaxis will be electric and they'll be powered by AI. So there are three of the major innovation platforms today converging in the form of one company. And so what that means if you can imagine is each one of these platforms is following its own S-curve slowly slowly then all at once right slowly slowly then all at once and now Tesla is really one S-curve feeding another S-curve feeding another S-curve which now that robotaxi has been launched we think is going to lead to explosive growth.
You consider robotaxi the ultimate AI project, don't you?
Yes, we think it is. We've called it the largest AI project on earth. And Elon Musk liked that on X. So that was good. But even beyond the robotaxi market and we have very little in our 2030 model for humanoid robots but that market is going to be even bigger and it is the convergence of the same three technologies robotics energy storage and AI it's just from a physical point of view the actuators are really the complicating story and getting the fingers which are more like puppets managed by actuators getting them to work and thread a needle that's more challenging but we think it's going to be an opportunity two to three times larger than the robotaxi platform opportunity.
Is that the reason why you always say that if you had to pick only one stock forever that would be Tesla?
Yes, it would. It would because not only robotaxis but then humanoid robots which extend the story dramatically for many many years.
Cathie, you have an incredible ability to anticipate the future almost as if you can see it unfolding before anyone else and I really envy that. But when it comes to understanding finance, I come from a pretty traditional background. I got my MBA and I was taught that the value of an asset is the discounted value of its future cash flows. Very very basic stuff. And the overarching paradigm in finance is the efficient market hypothesis in whatever form you look at it. Broadly speaking, prices are supposed to incorporate all available information, leaving little to no room to consistently beat the market. But obviously, your investment approaches suggests that the market isn't really able to properly discount disruptive innovation. So the question is how do you balance the traditional idea that prices are mostly right with the belief that there are still inefficiencies or let's say opportunities that aren't fully priced in yet.
Okay. So first I have to tell you that I'm standing on the shoulders of our chief futurist, our directors of research, our analysts, and our research associates. So there's a lot of work that goes into this. But what's also very important about that is in order to understand how big these opportunities are going to be, you have to define the opportunity correctly. So just let's go back to Tesla. In 2014, Tesla, directors of research in the traditional asset management world gave Tesla to the auto analyst. Now, the auto analyst was an expert at the internal combustion engine and human-driven cars, but knew nothing about robotics, energy storage, and AI. We have set up our research department very differently from traditional research. Our research team's responsibilities are broken out not by sector, not by industry, not by subindustry. Our analyst responsibilities are broken down by technology. And there are 15 different technologies out there involved in those five major platforms. And so we have 15 analysts focused on 15 different technologies collaborating because these technologies are converging. So we got Tesla right well before the traditional asset management world because they hadn't defined Tesla correctly. So I think that's very important.
The other reason I think that the market is extremely inefficient, I think it's gotten more and more inefficient over the years, is because of this move towards benchmark sensitive investing. And so if you put a benchmark at the center of your investment philosophy, then you are not really considering stocks that fall out of that benchmark. And guess what? Those are the stocks associated with companies who are creating the new world. So that is why investing has become less efficient over time. And interestingly yesterday I was on a call and an advisor said yeah I think that indexation is the equivalent of socialism. It's like socialism and what we're doing is the opposite of that. We're trying to figure out how the world is going to work, not manage how it is working and you know redistribute among a few indexes but actually you know go out there and see you know who's creating the new world right and what's so fascinating about this move towards passive and indexation is it has happened as the seeds for everything that is happening now. They were planted in the 20 years that ended in the tech and telecom bubble. They've been germinating for 25-30 years and now they're beginning to flourish. So, interestingly, in the late '90s, you had investors throwing money at the seeds. That did not end well. That ended badly. And now you have investors fearful of the future because the future doesn't fit into these benchmarks unless the benchmarks say that it does. And I'm happy to say some of our stocks are getting into benchmarks, but we're usually there four to five years earlier and we assume that they will get into the benchmarks. But between the time we get in and the time they get into the benchmarks, there's a huge amount of misperformance if you're just waiting for an index to bless your portfolio.
Yeah, it's remarkable that it took so long for Tesla, for example, to be included in the S&P 500 given the rules Standard and Poor's supplies for company inclusion. By the time it finally entered the index in 2020, it was already a massive company. So, I perfectly got the point.
Yes. Just one other thing I'd like to focus on here. We've talked a lot about sizing markets and the top-down part of our process. But your question really is about the bottom-up part of our process. Now I came out of the bottom-up stock research, stockpicking world. That's my entire history. I started managing money in 1990, and top-down investing was frowned upon. And therefore we lost a lot of top-down research, which was too bad because we do need that perspective. But in terms of bottom-up stock research-driven investing, we have six metrics, four of which we score, very important to innovation. Some of them are more qualitative, some more quantitative. I'll just give them very quickly. So the first one is management — people and the culture. Do we have a visionary management with a very strong point of view about the way the world is going to work, and really vision and willingness to stand up against short-term oriented shareholders who want their profits now, their dividends now, their share repurchases now, because it's so important to invest in these technologies.
The second is moats — barriers to entry. Where is the barrier to entry today in AI? Proprietary data is critical, domain expertise is critical, distribution is critical. And then execution — whereas other portfolio teams might be looking at operating margins to the decimal point, we are not. In fact, if a company says in a quarterly earnings report, you know what, we missed our operating margin guidance because we decided this opportunity is so important we needed to invest more — often a stock will go down 10, 20% on that one day. And if we are engaged with the company, have done our research, and like the direction they're going, we're happy they're investing more. So we will buy on a day like that. And then the fourth is product and service leadership. There are a lot of services that provide market share. There are a lot of quarterly earnings reports to help us piece the puzzle together. But there's also a lot of sentiment that we're getting on social media. That's real time. Any piece of research that we put out there, we get a lot of reactions. Some positive, some negative. All are welcome because we actually don't want to be wrong. We're putting our research out there not when it's finished but as it's evolving and we want to catch mistakes we're making. So those are the four that we score. You're asking primarily about valuation. But before valuation, I'll go to thesis risk.
