AI transcript
0:00:03 You manage more money than any other woman on Earth.
0:00:05 Across the company, we’re closing in on $40 billion.
0:00:08 She’s called one of the most disruptive and innovative forces.
0:00:10 Kathy Wood making some big headlines.
0:00:14 The ARK Innovation ETF soared over the early pandemic.
0:00:19 If I’m a believer in AI, what’s the number one stock that I should own?
0:00:22 I think everyone knows about NVIDIA.
0:00:25 We always try and answer that question with stocks
0:00:29 people are not thinking about in the right way.
0:00:30 So here’s the tough question.
0:00:34 If somebody else had your track record, would you invest in them?
0:00:35 Well.
0:00:47 Kathy Wood, you’re here.
0:00:48 I appreciate you doing this.
0:00:50 You’re a pretty remarkable person.
0:00:52 I’ve been watching you for a long time.
0:00:57 And there’s a good chance that you manage more money than any other woman on Earth.
0:00:59 As an active fund manager.
0:01:01 I don’t know if that’s exactly true, but you may be top five.
0:01:03 Yeah, probably.
0:01:03 I don’t know.
0:01:04 I don’t know myself.
0:01:06 I don’t have those members.
0:01:07 Yeah.
0:01:08 I’m curious, actually.
0:01:10 What’s the humble origin?
0:01:12 So what was Kathy Wood’s first job?
0:01:13 McDonald’s.
0:01:15 Cashier?
0:01:16 What were you doing?
0:01:16 Flippin’ Burgers?
0:01:18 No, I wasn’t.
0:01:19 I was at the register.
0:01:20 I was 16.
0:01:22 I also worked at a supermarket.
0:01:28 I first girl allowed to push in carts at Vaughn’s supermarket in Southern California.
0:01:33 Do you remember roughly what you were making when you worked at McDonald’s hourly?
0:01:33 Gosh.
0:01:34 I know.
0:01:38 Well, right before that, I was babysitting for a quarter an hour.
0:01:45 So you went from maybe a quarter an hour to managing something like $20, $30 billion in
0:01:45 a fund.
0:01:46 And I think this is interesting.
0:01:51 The reason I ask is because in the world of entrepreneurship, we always hear these hustle
0:01:52 stories.
0:01:56 And I don’t think you go from McDonald’s to the top where you’re at without hustle.
0:01:59 So what’s the hustle story you pride yourself on?
0:02:03 Well, the first big break was getting into the business.
0:02:09 Art Laffer, I’m not sure if you know Laffer, Laffer Curve, Supply Side Economics, Reaganomics.
0:02:13 He was my professor at the University of Southern California.
0:02:15 He was like an advisor to presidents, right?
0:02:23 Oh, every president since Richard Nixon, except for presidents Obama and Biden.
0:02:26 And, you know, he was agnostic.
0:02:35 If anyone, didn’t matter what party, wanted to hear what he had to say about taxes, deregulation,
0:02:41 monetary policy, he wanted to give his point of view.
0:02:50 And, you know, we’ve come full circle, Art and I, because I and my team introduced Art
0:02:52 in 2015 to Bitcoin.
0:03:00 And when he read our paper, he said, this is what I’ve been waiting for since the U.S.
0:03:07 closed the gold window in 1971, a global rules-based monetary system.
0:03:14 Wrong rule, quantity theory of money, you know, limited to 21 million units, but we’ll
0:03:15 get there.
0:03:18 And, of course, he was talking about stable coins.
0:03:24 So now we have introduced him to stable coins, tether, circle, and so forth.
0:03:26 And he said, ah, the right rule.
0:03:31 Have you seen this website, WTF happened in 1971?
0:03:33 It’s amazing.
0:03:36 There’s an entire website basically saying, what the F happened in 1971?
0:03:42 And it shows like a series of charts where something happened in 1971 and the world was never really
0:03:43 never the same.
0:03:45 And it’s just a, it’s a very compelling case.
0:03:46 It makes you want to go look at it.
0:03:49 And obviously, I think that’s the year that we went off the gold standard, right?
0:03:55 It’s the year we went off the gold standard and all hell broke loose in monetary policy.
0:03:57 We went into massive inflation.
0:04:04 So anyway, it was in the late 70s that while I was in his class that Art introduced me to
0:04:05 Capital Group.
0:04:07 I walked into Capital Group.
0:04:09 I didn’t even know what the investment business was.
0:04:11 I had been a waitress.
0:04:16 I was interested in economics, but I didn’t know this business.
0:04:22 And Capital was the premier firm in Southern California at the time.
0:04:24 Sounds like that might be a tough job to get.
0:04:32 Art recommended me highly to Don Conlon, who was the chief economist of Capital Group.
0:04:42 And I walked in there and Don was losing a person who was going on to Harvard Business School.
0:04:45 So this woman, her name was Claudia Huntington.
0:04:48 She was so good at what she did.
0:04:52 He was looking for one and a half people to replace her.
0:04:55 I was the half, but I didn’t want to be the half.
0:04:57 I wanted to be the one and a half.
0:05:01 So let me ask you a question about that, because I think everybody in their career, you know,
0:05:02 will have an opportunity.
0:05:05 I had one when I moved to San Francisco when I was 24.
0:05:07 I didn’t know anybody, but I wanted to be an entrepreneur.
0:05:08 I wanted to be in Silicon Valley.
0:05:11 This billionaire was hiring for this role.
0:05:12 I don’t know how I got the job.
0:05:15 I was, they literally told me, you’re not qualified for this, but we like you.
0:05:16 We’ll bring you on.
0:05:19 We’ll still hire somebody else qualified for that role, but we want you here anyway.
0:05:23 So I had like my foot in the door and I think everybody has this opportunity to work hard,
0:05:25 but it’s, there’s one thing to put in hours.
0:05:29 So I think there’s a lesson in like first one to be there, last one to leave, like put in
0:05:30 a sheer number of hours.
0:05:36 But what else goes into kind of like making an impression during that like sprint phase of
0:05:38 your career when you can just like fully go full force?
0:05:42 What else besides sitting in the chair for a lot of hours matters?
0:05:44 Do you think, you know, what’s the mindset?
0:05:54 Sitting in the chair maybe matters, but I think the most important thing is, and my objective
0:05:58 was to bring new technology into the firm.
0:06:01 I was using economics time-sharing system.
0:06:02 We were back in time-sharing.
0:06:06 All the charts that you just brought up, boom, boom, boom, boom.
0:06:15 Back then, each one would have cost, in today’s dollars, $5,000 to $10,000.
0:06:18 So that just gives you a sense of how far we’ve come.
0:06:26 But sparingly, I was able to use charts for, you know, ones we made up, so original, and
0:06:34 then, you know, call them from people we trusted and really develop little economic books for
0:06:38 and presentations for Don to use.
0:06:42 Yeah, you’ll get what you want when you help other people get what they want.
0:06:45 So the fastest way to getting what you want is just to give other people what they want.
0:06:49 And I like what you’re pointing out, which is that as a young person, you’re coming in
0:06:54 without the experience, without the network, without maybe the track record or any of those
0:06:54 things.
0:06:59 Those are your disadvantages, but maybe your advantage is tech and new things might be easier
0:07:02 for you to pick up because you have time and maybe you grew up with those tools and you’re
0:07:03 less set in old ways.
0:07:06 And so you bring something to the table and that can be your thing.
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0:07:35 All right, let me get back to the episode.
0:07:40 I’m just curious, like what is a day in the life of Cathie Wood look like?
0:07:45 What’s your actual day-to-day main thing that you focus on?
0:07:51 I hold sacred in terms of from the moment I get up in the morning until 1030.
0:07:56 That time is all about research.
0:08:04 And so we have our research meeting from nine in the morning to 1030.
0:08:13 First half of it is just the entire research team together and investment team, portfolio
0:08:18 management teams together, really sharing information.
0:08:24 And then we focus on, in the last half hour, on one of the four teams.
0:08:36 So we’re broken up into autonomous technology and robotics team, AI and cloud, which has forked
0:08:43 another team, which is consumer internet and fintech.
0:08:50 Then we have our multi-omics team, which is really all about life sciences and how profoundly
0:08:52 AI is going to transform healthcare.
0:08:55 And I think that’s the most inefficiently priced part of the market.
0:08:59 And then we have our blockchain technology team.
0:09:06 On Fridays at 1030, we have a brainstorm.
0:09:11 And the brainstorm is all our teams coming together or staying together.
0:09:19 But we have another, I’m going to say, another 40 people who have followed us over the years
0:09:21 and are passionate about innovation.
0:09:25 And we invite them to what’s called a brainstorm.
0:09:31 And that is where we try to get out of this not invented here.
0:09:33 We really want pushback.
0:09:41 These are venture capitalists, they’re entrepreneurs, they’re retired engineers, they’re retired professors,
0:09:46 they’re people teaching in universities today.
0:09:54 And they are very vocal because they’re every, all of us, they probably more for their personal
0:09:58 accounts, but we’re all trying to figure out how the world is going to work.
0:10:04 And we’re trying to push the frontiers of knowledge forward as fast as we can and anticipate what
0:10:10 the next set of topics are going to be that people are discussing and trying to figure out
0:10:12 where we should position ourselves.
0:10:13 That’s pretty interesting.
0:10:14 Is that common?
0:10:19 Do you do other firms do this kind of Friday open door brainstorm with like external folks?
0:10:20 That sounds pretty unique.
0:10:22 No, they don’t.
0:10:28 And I’ve done this since 2001 when I was at my last firm.
0:10:35 I just thought it was really important not to get stuck in our own research, but to have
0:10:36 it battle tested.
0:10:44 And we took that to another level when I founded ARC with this notion that we’re going to give
0:10:45 our research away.
0:10:50 We’re going to give our research away, not when it’s finished, because it’s never finished,
0:10:54 but as it is evolving and we push it out.
0:11:00 Now, at the time, 2014, Twitter was for tweens, teens, and celebrities, right?
0:11:04 So I didn’t think that was going to be our primary social network.
0:11:07 We thought maybe LinkedIn would be.
0:11:13 Instead, X has become the most important social network, even for crypto.
0:11:17 I thought Telegram was where all of that was going to live.
0:11:23 And yeah, there are all kinds of conversations, but the ones that we need and that I need to
0:11:24 see, they are on X.
0:11:30 And sometimes we stir the pot, you know, with our research and get debates going.
0:11:36 So I feel that the world is moving so quickly today.
0:11:39 It’s not like it was in 1977.
0:11:49 Back in 1977, as I described, it was really expensive to get information and to travel places
0:11:52 to pull information from management.
0:11:58 And so research departments like the one at Capitol, they were closed and that was their
0:11:59 secret sauce.
0:12:04 Today, information is ubiquitous and it is all over the place.
0:12:07 In fact, you have to figure out, is it real or fake?
0:12:10 You know, so, you know, so that takes another skill.
0:12:21 So I thought, you know, the closed world is probably not where we are best suited for what we want
0:12:28 to do, and that is focus exclusively on technologically enabled disruptive innovation.
0:12:30 That’s all we want to do.
0:12:36 Well, there’s so much information out there and we knew we could harness it.
0:12:44 And it’s how you put it together and what you place priorities on in terms of the kind of
0:12:49 information and the kinds of assumptions that you’re making that become more important.
0:12:56 And you, this might be a dumb question, but like, you will go on TV and you’ll say, I
0:13:00 think Tesla’s going to $2,000 a share or whatever your target price is.
0:13:03 And everyone says, oh my gosh, that’s really bullish.
0:13:07 And you say, here’s why we believe, here’s what, you know, here’s what we believe the future
0:13:07 looks like.
0:13:10 And I hold Tesla and I hope that that all comes true.
0:13:12 But you are very active.
0:13:14 Like you’re buying and selling Tesla all the time.
0:13:18 I looked in the last like 24 hours, your firm has made like 20 trades or something like
0:13:18 that.
0:13:21 Like millions of dollars in and out of these positions.
0:13:25 If you believe Tesla’s going to, you know, some $2,000 a share.
0:13:28 Why don’t you just buy it and hold it?
0:13:30 What is all the active trading for?
0:13:32 And like, are you day trading?
0:13:36 I mean, I’m not from the investment world, so I’m trying to understand, you know, you have
0:13:39 sort of the Buffett mentality and then, you know, you’re very, very active.
0:13:40 I don’t really get that.
0:13:43 Yeah, that’s another great question.
0:13:45 I know it must seem confusing.
0:13:54 So, and we often do describe ourselves, a few people believe this, but we believe it, as
0:13:59 a deep value manager like a Warren Buffett, if you give us five years.
0:14:08 And, you know, Warren Buffett, he was the first to admit, I don’t invest in technology.
0:14:09 He made a few good ones.
0:14:10 Like Apple was great.
0:14:17 IBM not so, but he knew where his strengths were, or he knows where his strengths are.
0:14:18 He’s still with us.
0:14:26 And he did not feel that technology was where he had an edge.
0:14:28 That’s where we do have our edge.
0:14:33 And so, you can say we’re a great complement to the Warren Buffett strategy if you give us
0:14:35 a five-year investment time horizon.
0:14:38 So, why do we trade so much?
0:14:47 Well, because of what has happened to the markets, and really since I got into the business, I think
0:14:54 more than 75% of the trading is algorithmic and high-frequency trading.
0:15:01 There’s a huge amount of volatility in the market itself, but especially in our stocks.
0:15:09 So, if you look at our trading in Tesla, we are using it, we’re using the volatility to our advantage.
0:15:17 So, rarely has Tesla dropped below the number one position in our flagship portfolio, ARKK.
0:15:29 What has happened, it has gone from $100 to $500 and becomes, you know, 13%, 14% of the portfolio.
0:15:37 So, from a portfolio management point of view, we are effectively rebalancing.
0:15:40 That’s a lot of hard work, $100 to $500.
0:15:46 And we know, we know, we know, we know, we know, Tesla is a controversial stock.
0:15:50 Elon Musk is a controversial individual.
0:15:56 And we are going to have opportunities at lower prices to move back in.
0:16:02 And so, that’s the kind of trading that you see around our high-conviction stocks.
0:16:03 Okay.
0:16:04 So, let’s take what you just said.
0:16:07 So, you said, give us five years, right?
0:16:12 Because we’re betting on these sort of long-term technology S-curves that we think are playing out.
0:16:14 So, here’s the tough question.
0:16:22 Over the last 10 years, you’ve been making hundreds of millions in fees, but haven’t outperformed the simple index like QQQ.
0:16:24 Do you think that’s a fair criticism?
0:16:28 So, can I give you a reset here?
0:16:29 I love the question.
0:16:37 You’re giving me an opportunity to answer a question that I know is on many people’s minds, even if they don’t ask it.
0:16:37 So, thank you.
0:16:53 So, our objective as a firm is to deliver a minimum 15% compound annual rate of return over five years.
0:16:55 So, you are absolutely right.
0:16:57 We have not done that.
0:17:00 We have done that since inception.
0:17:07 So, since inception, our compound annual rate of return is over 15%.
0:17:11 Now, what happened in the middle there?
0:17:17 Because you’re focused on endpoint sensitivity, and I understand why people use five years, ten years, all of that.
0:17:17 Sure.
0:17:29 If you use ten years, and you look at Morningstar, and just their quantitative metrics, which have no human input.
0:17:31 They just have their rules-based system.
0:17:36 Based on the benchmark they selected for us, we didn’t choose the benchmark.
0:17:38 We are benchmark agnostic.
0:17:45 We are in the fourth percentile of performance for that benchmark.
0:17:48 Now, that benchmark is mid-cap growth.
0:17:49 Right.
0:18:00 Which kind of fits, because we consider ourselves all-cap, but, you know, if you average, you’ll get mid-ish cap, let’s say.
0:18:02 So, that’s good.
0:18:06 You know, that’s actually saying something.
0:18:17 The space we’ve been in, anything less than large-cap, and especially mega-cap growth, especially in the tech space, has been very tough.
0:18:20 Okay, so that’s another marker.
0:18:23 Now, what about the last five years?
0:18:31 Well, we had COVID in 2020, because this is when we blew up.
0:18:42 I’m not sure if you know this part of the history, but because we were the only investment firm putting our research out on social media,
0:18:53 and the only one posting our trades every day, we went viral during COVID, because everyone was sitting in front of their computers trying to figure out what to do with their time, right?
0:18:55 And their extra money, by the way.
0:18:57 Yeah, and their extra money.
0:19:09 We were actually really one of the few out there teaching people about investing, bringing them along on our journey.
0:19:13 In 2020, we were up 150%.
0:19:17 And at the end of that year, remember, we’re five years away from that.
0:19:19 This is what we’re comparing against.
0:19:24 At the end of that year, I was on Eric Schatzker’s show on Bloomberg.
0:19:29 It was a holiday show, and they gave us a lot of time.
0:19:34 And one of my main messages was, hey, keep some powder dry.
0:19:40 This, we know what goes up like this is going to come down.
0:19:47 It’s just too much capital chasing the opportunity, perhaps too soon.
0:19:56 And that last point, I probably should have said more loudly to myself and to our team.
0:19:57 Right.
0:20:11 Because even though our modeling stock by stock got us to a 15% compound annual rate of return over the next five years, which was very low.
0:20:18 Normally, we’re expecting 25% to 40% compound annual rate of return.
0:20:23 So it had dropped because of the appreciation in stocks to 15%.
0:20:32 But what also had happened and what we did not appreciate enough was many people think, oh, the interest rate increase.
0:20:34 That wasn’t as much the problem.
0:20:39 The problem was supply chain bottlenecks.
0:20:44 Our models are driven by unit growth.
0:20:54 And when there’s an interruption in unit growth, our model, the rate of return expectations come down.
0:21:05 And I thought and we thought we were going to come out of this crisis in a V-shaped recovery.
0:21:06 And we did.
0:21:07 That was correct.
0:21:15 But we didn’t catch how long it was going to take supply chains to reorient.
0:21:21 And that, I think, was a big, big lesson for us.
0:21:36 If I had just focused on that one variable, I would have said, all right, let’s move more into larger cap tech stocks with a big cash position that are innovating.
0:21:39 And it would have been the MAG-6 and all of that.
0:21:41 We did not do that.
0:21:43 What we did, we owned them.
0:21:55 And because we had started doing that in the bull market, we always diversify as a bull market extends because our stocks do tend to go crazy to the upside.
0:21:56 So we were already doing that.
0:22:08 But then in 21, those stocks kept going up and our stocks, so smaller cap and mid cap stocks, started going down.
0:22:11 And so what we always do is rebalance.
0:22:12 We took profits there.
0:22:13 We bought.
0:22:15 That was just way too soon.
0:22:21 It worked out, and I think history will show that everything is fine.
0:22:23 We had to have people stick with us.
0:22:34 And there are so many people who piled in at the top, even though we’re saying, hold your horses, and who left us at the bottom, which is classic.
0:22:36 It’s classic.
0:22:54 And so we’re going to be out there in this cycle a lot more saying along the way, rebalance, sell, take profits, so that when our strategies go through a weak spot, a sinking spell, then you’ll have the psychological wherewithal to buy.
0:22:58 It’s called rebalancing, and it’s a basic investment concept.
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0:23:27 Betting against you as betting against a combination of things that I would never want to bet against.
0:23:28 It’s betting against AI.
0:23:30 It’s betting against crypto.
0:23:31 It’s betting against innovation.
0:23:32 And it’s betting against Elon Musk.
0:23:36 Those are just, that’s not who I, you know, that’s like the Monstars in Space Jam.
0:23:38 I’m just not trying to bet against that.
0:23:43 You might even be right on some, on any one of those individual things, maybe for a period of time.
0:23:45 It’s just not where I would want to be positioned against long-term.
0:23:52 At the same time, like, I’m a normal person, and it’s pretty crazy when, like, this is invention.
0:23:54 In venture capital, too, by the way.
0:23:57 In venture capital, VCs, it’s a rigged game.
0:24:00 I heard this a long time ago, and it never left me, which is venture capital is a rigged game.
0:24:05 You make 2% annually on your fees, regardless of whether you make money or lose money.
0:24:07 And I think what you do is very similar, right?
0:24:13 Like, if you have, like, I don’t know, let’s just use a round number, $20 billion in assets across your ETFs.
0:24:14 Is that about right?
0:24:16 Across the company, we’re closing in on $40 billion.
0:24:20 Okay, that includes the digital assets, private funds, everything?
0:24:24 Yes, and so we do have a venture fund, so I know what you’re talking about.
0:24:27 We’re doing ours a little differently.
0:24:37 We don’t have a carry, so that anyone with $500 can get onto the cap tables of SpaceX, OpenAI, Neuralink, and so forth.
0:24:40 You don’t have carry, so how do you make money in that, just on the fees?
0:24:43 So what we do is we do have a high fee.
0:24:48 So I think it’s 2.75%.
0:25:04 And what we did to arrive there is we said, okay, what do the best venture capital firms in the world deliver over time in terms of compound annual rate of return for their clients?
0:25:08 And how much of the benefit do they derive?
0:25:23 And the best ones, and maybe they’re going to be north of 2.75% per year on average, and especially in this kind of a market where AI is just out of sight.
0:25:35 But the best ones, if you do a very long-term historical retrospective, the historical is retrospective, 2.75% was the landing point.
0:25:38 But we are offering direct-to-cap table.
0:25:43 These are not SPV, not SPVs, so there are no fees upon fees upon fees.
0:26:03 Right, and so I think that the challenge is basically on $20 or $40 billion in total assets, there’s a guarantee, this is why finance and fund management is one of the best businesses in the world, is you get hundreds of millions in fees guaranteed regardless of whether you’re up, whether you’re down.
0:26:07 And then the more you go up, maybe you have additional carrier.
0:26:10 There’s other things in venture capital that you benefit from.
0:26:13 And so I think that’s the challenge, right?
0:26:15 Like Munger used to say, you know, show me your incentive, I’ll show you your outcome.
0:26:22 It’s like, I think all finance and fund management is generally suited towards grow assets under management.
0:26:26 We make money either way, and then like try to do your best in terms of performance.
0:26:30 And in the long run, you know, you will be judged on the performance, but in the short run, it’s hard to tell, right?
0:26:41 Like what’s working, what’s not, which is why when Buffett, I think, started, he basically said, I’ll take nothing for the first 6%, which I think was like kind of the sort of the index standard at the time.
0:26:46 And he said, but if I beat market, then I want 25% of profits.
0:26:49 And I thought like, you know, that was a great structure.
0:26:53 And I think the world of finance has moved away from that because why would you if you could – I would do it too.
0:26:54 If I could take the guaranteed fees, I’m going to take it.
0:27:05 Well, you know, it’s interesting from the – I’m going to say from the 80s on, when I saw hedge fund structures and venture capital, I said, I’m an economist.
0:27:08 I said, oh, that game’s going to end.
0:27:16 And that’s, you know, that – those kinds of excesses, excessive returns, shall we say, they go away with competition.
0:27:27 But in the venture world, and there is a huge amount of competition, but if you look at where the real money is made, it’s in the top, you know, 10.
0:27:33 The top 10 is where a disproportionate amount of the returns are.
0:27:35 And, of course, that’s what we’re aspiring to.
0:27:37 And, of course, everyone is aspiring to it.
0:27:46 But there’s some kind of network effect, and I think it has to do with the network effect is not a viral app.
0:27:47 It is the community.
0:27:58 Well, venture has one different property than what you do outside of your venture stuff, which is in startups, it’s the only asset class where the security selects the investor.
0:28:04 So, you know, for Buffett or any public stock market, I get to just pick what I want to be in, and I push a button, I’m in the stock.
0:28:11 Whereas in venture, the hot startups want to be with the hot funds, and only the hot funds get to be on the cap table.
0:28:14 So the security selects the investor, not just the investor selecting the security.
0:28:16 So it has this, like, that’s where the network effect comes in.
0:28:25 That’s where the brand effects come in, and that’s why Sequoia and Benchmark and these other guys will keep showing up in the top because if I’m one of the top startups, I want them.
0:28:27 And so they get the access.
0:28:31 Even if another investor was totally right in their thesis, they just can’t get in the cap table.
0:28:35 Yeah, it’s a self—yes, there is self-selection.
0:28:39 You’re seeing in the hedge fund world big changes, though.
0:28:46 In that world, the fee structure is changing because passive, the indexes, were outperforming active.
0:28:50 It was a self-fulfilling prophecy because the pendulum was swinging there.
0:29:00 I believe that that pendulum swing, I think, I think that, and consider the source, right?
0:29:11 But I think the pendulum swing, the final swoosh in that direction, was in the last few years towards the Mag-6.
0:29:21 And now they’re so—one of the reasons they’re such concentrated parts of the—now, are they all going to benefit from all of these new technologies?
0:29:26 You know, our focus on robotics, energy storage, AI, blockchain technology, and multiomics?
0:29:28 Some of them will.
0:29:30 Apple, we’ve been watching for a long time.
0:29:31 Finally, it’s out.
0:29:33 They don’t know what they’re doing in AI.
0:29:36 Now I think they’re scrambling a bit.
0:29:38 So, you know, we’ll see what happens.
0:29:41 Each one of them has a weakness.
0:29:44 Each of the Mag-6 has a weak spot.
0:29:54 Sure, they’ll participate in the wave, but they also have some weaknesses caused by the disruptions associated with these new ways of doing things.
0:29:59 And I think we’re at the beginning of that pendulum swing in the other direction.
0:30:01 As I just said, consider the source.
0:30:05 That would be great for us because we don’t own the Mag-6 in our top 10.
0:30:10 It’s not like we won’t own them, but we don’t own them for the most part in the top 10.
0:30:20 If I’m a believer in AI, what’s the number one stock that I should own to benefit from the oncoming AI wave?
0:30:27 Well, I think everyone knows about NVIDIA.
0:30:31 We always try and answer that question with stocks.
0:30:37 People, either they don’t know or they’re not quite thinking about them in the right way.
0:30:39 Yeah, misunderstood.
0:30:42 Maybe not unknown, but misunderstood or mispriced.
0:30:43 So, yes.
0:31:01 As we were selling NVIDIA and we got all kinds of flack, nobody bothered to notice that we put it in the portfolio in 2014 because of autonomous driving at, I think, 20 cents on the current stock’s basis at 20 cents per share.
0:31:05 And we held it for years and we held it for years and no one would listen to us.
0:31:06 No one.
0:31:13 I talked about robotics, talked about autonomous driving, talked about, nope, it was a PC gaming chip company and that’s all it was.
0:31:24 And then it explodes with ChatGPT and, you know, we start selling it and we sold it too soon in the flagship.
0:31:26 I mean, meaning we exited it.
0:31:29 We’re back in it now when it dropped during tariff turmoil.
0:31:39 In portfolio management, you have to not look at just what was sold, but what did you do with the proceeds?
0:31:45 How about Palantir, which I think from that point has done better than NVIDIA?
0:31:48 I don’t know, it was, that was the case at one point.
0:31:51 How about Coinbase when the SEC was suing it?
0:31:55 That’s one of the, that’s what we use some of the NVIDIA for.
0:32:00 It has done pretty darn well, I think almost as well as NVIDIA.
0:32:01 So you have to do it.
0:32:07 So today, of course, NVIDIA, I mean, it still has a very important role.
0:32:10 Palantir still has a very important role.
0:32:14 It is the premier platform as a service company.
0:32:18 We think embodied AI is underappreciated.
0:32:18 What is that?
0:32:22 Embodied AI is physical AI, physical and digital worlds meeting.
0:32:24 You know what I’m going to say next.
0:32:29 Tesla is the largest AI project on earth.
0:32:32 And it’s not just robo-taxis anymore.
0:32:34 It is humanoid robots.
0:32:36 It is humanoid robots.
0:32:43 And according to our research, while the robo-taxi opportunity globally for everyone, including China,
0:32:51 is an $8 to $10 trillion revenue opportunity in the next 5 to 10 years from maybe a billion now.
0:32:52 So think about that.
0:32:59 A billion to $8 to $10 trillion, the whole ecosystem, with the platform companies like Tesla getting half of that.
0:33:01 So that’s $4 to $4 to $5 trillion.
0:33:03 That’s a big market.
0:33:18 According to our estimates, the humanoid robot market will be a $26 trillion market in the next, I’ll say, 7 to 15 years.
0:33:24 So, me and Tyler, the CEO of Beehive, came up with a little challenge for you.
0:33:26 It’s the newsletter challenge.
0:33:28 Now, if you know me, you know that I’m a big fan of newsletters.
0:33:29 I got my own newsletter.
0:33:32 I also had a business that was a newsletter business that was amazing.
0:33:33 I wrote this newsletter about crypto.
0:33:36 We grew it to a quarter million subscribers.
0:33:38 And we ended up selling it after a year for millions of dollars.
0:33:40 And I want you to be able to do the same thing in your business.
0:33:42 So, we’re doing a challenge.
0:33:43 $10,000 is on the line.
0:33:46 Plus, me and Tyler will actually be in your corner as growth advisors.
0:33:49 You just need to go to beehive.com slash MFM.
0:33:52 And you either start a new newsletter or you move your current newsletter over there.
0:33:54 And five finalists will get picked to pitch.
0:33:55 Me and Tyler, sort of like Shark Tank.
0:33:57 And the winner gets $10,000.
0:33:59 So, go to beehive.com slash MFM.
0:34:02 That’s beehive.com slash MFM to enter the challenge today.
0:34:07 I wanted to ask you about this because you put out this great deck or ARC put out a great deck.
0:34:10 And I love this slide.
0:34:12 So, if you’re on YouTube, you’ll be able to see this.
0:34:14 If you’re on audio, sorry.
0:34:16 You know, go to YouTube or Spotify and check this out.
0:34:19 So, on this slide, I’ll just describe it.
0:34:21 So, it’s basically the cost per mile.
0:34:25 Like, how much does it cost to travel, to transport a human being one mile?
0:34:29 And you started like in the 1800s, like horse and carriage.
0:34:31 And, you know, adjusted for inflation and all that.
0:34:36 It looks like it’s $2.10 to travel a mile when you’re on horse and carriage.
0:34:40 Then, you know, you get the Ford, you know, Henry Ford era.
0:34:42 And you’re at $1.10.
0:34:47 And basically, for like, I don’t know, almost 100 years, it’s been roughly the same number.
0:34:49 It’s been $1.10 to travel a mile.
0:34:59 And then your estimate is that with self-driving cars, where you don’t have a driver in there and you’re on an electric self-driving car, that the cost per mile could drop to, your estimate, is a quarter.
0:35:05 So, you know, four times cheaper than what it currently costs and what it has cost for the last 100 years.
0:35:06 Did I summarize your slide correctly?
0:35:08 That is correct.
0:35:09 That is correct.
0:35:14 And, you know, when we first did this research, we too were astonished with that.
0:35:14 Wait a minute.
0:35:16 It cost the same?
0:35:17 Inflation adjusted?
0:35:26 And then one of the reasons for that is because the automobile matured fairly quickly, right?
0:35:28 And we’re all about Wright’s law.
0:35:32 Wright’s law tries to understand, okay, you’ve got this new technology.
0:35:35 You’re starting from a low base.
0:35:47 For every cumulative doubling in that base, so from one to two, two to four, four to eight, for every cumulative doubling, cost decline at a consistent percentage rate for each technology.
0:35:51 Well, the internal combustion engine is mature.
0:36:07 And so it has no shot against EVs, even though I know that’s not the prevailing wisdom in this political climate, or I’m just using economics and learning curves, so technology.
0:36:09 Sorry, what do you mean by it has no shot?
0:36:11 You mean, like, no shot in what sense?
0:36:13 The cost comparison or just—
0:36:16 Nope, because of the chart you just showed.
0:36:19 You can’t get that cost down any lower.
0:36:23 Heck, there are no more cumulative doublings.
0:36:24 Everybody’s got one.
0:36:27 You know, it’s a bit of an exaggeration in the emerging markets.
0:36:34 They don’t, but they’re not going to be paying up for internal—they’re going to be looking for the cheapest solutions to cars, and those are going to be electric.
0:36:42 And the part that I didn’t get was—so, okay, great, the cost is going to go down because it’s a self-driving electric vehicle.
0:36:43 Okay, I get that.
0:36:45 I could see why the cost goes down.
0:36:48 And I assume when the cost goes down, the demand goes up.
0:36:50 It’s cheaper to travel, more people travel.
0:36:54 It gets selected over all the other more expensive ways to travel.
0:37:04 But the estimate you have, where it’s like the cyber taxi revenue, I think you have the autonomous revenue is sort of like in the—what did you say?
0:37:05 You said $10 trillion or something like that?
0:37:05 $10 trillion.
0:37:06 Is it global?
0:37:06 Yeah, yeah.
0:37:17 So right now, if I just take Uber, Lyft, DoorDash, like kind of the revenue of all those companies, which today—I would say ride-sharing is not like a new idea.
0:37:18 It’s pretty prevalent.
0:37:20 I think Uber’s at $40 billion.
0:37:22 You add Lyft, that’s another $6 billion.
0:37:24 And then DoorDash, it does about $10 billion.
0:37:29 So like the total of all three of those companies is only in the like $50, $60 billion range.
0:37:31 But you’re saying that the—
0:37:34 But that is even different from what we’re talking about here.
0:37:41 They are not autonomous, and they are not in the pole position—I mean, DoorDash will harness autonomous.
0:37:49 That’s a very interesting one because we think delivery is with—especially with drones and rolling robots and everything is a very interesting use case.
0:37:53 But Uber and Lyft are not in the pole position for this new world.
0:37:54 Right, right.
0:38:01 But I guess what I’m saying is that’s what’s spent today on taking rides from a ride—like a push-a-button-get-a-ride service.
0:38:03 Oh, I see what you’re saying.
0:38:07 Why is self-driving going to be 20 times more revenue generated?
0:38:11 Why is it going to be $10 trillion when all of those add up to $60 billion?
0:38:11 Right.
0:38:23 What we’re doing is moving from a very narrow subset of transportation called ride-hail today to all of transportation, right?
0:38:29 So we’re moving the entire market to autonomous to get that number.
0:38:33 They’re a very small slice, very, very, very small slice.
0:38:49 And in fact, what’s so interesting in San Francisco, I think that Waymo, we’re finding, research is showing, that people are willing to wait longer and pay more for a Waymo than for an Uber or Lyft.
0:39:12 And I believe this has already happened, the number of miles, even though in San Francisco Waymo is geofenced and Lyft is not, the number of miles in San Francisco, the San Francisco metropolitan area, that Waymo is driving per day has surpassed Lyft and is heading for Uber.
0:39:13 Isn’t that remarkable?
0:39:15 People are willing to pay up.
0:39:18 Now, that’s not in our $8 to $10 trillion.
0:39:32 We assume that, sure, they’ll start maybe right at or below the prevailing prices for Uber and Lyft, but they will drop over time to that $0.25.
0:39:47 So starting in the $2, and it’s surge pricing, you know, it can be $8 per mile, starting $2-ish, $2.50 maybe, and dropping to $0.25.
0:39:50 $0.25 is at scale, right?
0:39:52 So that’s the $8 to $10 trillion.
0:39:55 And, you know, just think about it.
0:40:07 I mean, I would prefer to take an Uber today, even though I have two Teslas, and I love them, but I still have to pay some attention on the road, right?
0:40:08 Right.
0:40:14 I’m curious, how much of the Tesla market cap, I think Tesla’s $1.3 or $1.4 trillion.
0:40:16 How much of that is Elon?
0:40:25 Meaning, if I took Elon off the company, if Elon went to sleep for the next 20 years, and we weren’t going to have Elon running the company, would you keep your position the same way?
0:40:27 And I guess it’s a pie chart, right?
0:40:31 Of that $1.3 trillion, what do you think that number goes to if there’s no Elon?
0:40:37 Well, if you had asked the question like five years ago, the answer would have been different.
0:40:54 But I think what has happened, and one of the reasons Elon has spent so much time doing other things, some of which people didn’t agree with, is because I think he feels they have pretty much solved the last mile in FSD.
0:41:00 And if they have done that, then they’re going to capture the robo-taxi opportunity.
0:41:02 They’re going to be able to scale.
0:41:14 We would not, however, start incorporating humanoid robots in the way we – we haven’t done much yet in our $2,600 price target, and we’ll update that.
0:41:18 We usually update it each spring for public consumption.
0:41:21 And so we have very little for humanoid.
0:41:34 We’d probably be much less optimistic on humanoid robots, and so we wouldn’t put as much in as we perhaps will with Elon at the helm.
0:41:35 Right.
0:41:36 Wonderful.
0:41:37 Well, Kathy, I appreciate you coming on.
0:41:39 It was fun to hear some of your stories.
0:41:42 It was good to hear your take on some of the tougher questions.
0:41:44 So I appreciate you doing this.
0:41:47 Yeah, thank you, Sean, and thank you for the tougher questions.
0:41:55 They’re important – thank you for giving me a platform on which to answer those questions because it is important.
0:41:56 It is important.
0:41:56 Great.
0:41:57 Well, thank you.
0:41:57 I hope to do it again.
0:42:00 I feel like I can rule the world.
0:42:02 I know I could be what I want to.
0:42:05 I put my all in it like no day’s off.
0:42:07 On the road, let’s travel, never looking back.
0:42:08 All right.
0:42:12 Let’s take a quick break because, as you know, we are on the HubSpot Podcast Network, but we’re not the only ones.
0:42:15 There’s other podcasts on this network, too, and maybe you liked them.
0:42:15 Maybe you should check them out.
0:42:18 One of them that I want to draw your attention to is called Nudge by Phil Agnew.
0:42:29 And whether you’re a marketer or a salesperson and you’re looking for the small changes you could make, the new habits you could do, the small decisions you could make that will make a big difference, that’s what that podcast is all about.
0:42:30 Check it out.
0:42:32 It’s called Nudge, and you can get it wherever you get your podcasts.
0:00:05 Across the company, we’re closing in on $40 billion.
0:00:08 She’s called one of the most disruptive and innovative forces.
0:00:10 Kathy Wood making some big headlines.
0:00:14 The ARK Innovation ETF soared over the early pandemic.
0:00:19 If I’m a believer in AI, what’s the number one stock that I should own?
0:00:22 I think everyone knows about NVIDIA.
0:00:25 We always try and answer that question with stocks
0:00:29 people are not thinking about in the right way.
0:00:30 So here’s the tough question.
0:00:34 If somebody else had your track record, would you invest in them?
0:00:35 Well.
0:00:47 Kathy Wood, you’re here.
0:00:48 I appreciate you doing this.
0:00:50 You’re a pretty remarkable person.
0:00:52 I’ve been watching you for a long time.
0:00:57 And there’s a good chance that you manage more money than any other woman on Earth.
0:00:59 As an active fund manager.
0:01:01 I don’t know if that’s exactly true, but you may be top five.
0:01:03 Yeah, probably.
0:01:03 I don’t know.
0:01:04 I don’t know myself.
0:01:06 I don’t have those members.
0:01:07 Yeah.
0:01:08 I’m curious, actually.
0:01:10 What’s the humble origin?
0:01:12 So what was Kathy Wood’s first job?
0:01:13 McDonald’s.
0:01:15 Cashier?
0:01:16 What were you doing?
0:01:16 Flippin’ Burgers?
0:01:18 No, I wasn’t.
0:01:19 I was at the register.
0:01:20 I was 16.
0:01:22 I also worked at a supermarket.
0:01:28 I first girl allowed to push in carts at Vaughn’s supermarket in Southern California.
0:01:33 Do you remember roughly what you were making when you worked at McDonald’s hourly?
0:01:33 Gosh.
0:01:34 I know.
0:01:38 Well, right before that, I was babysitting for a quarter an hour.
0:01:45 So you went from maybe a quarter an hour to managing something like $20, $30 billion in
0:01:45 a fund.
0:01:46 And I think this is interesting.
0:01:51 The reason I ask is because in the world of entrepreneurship, we always hear these hustle
0:01:52 stories.
0:01:56 And I don’t think you go from McDonald’s to the top where you’re at without hustle.
0:01:59 So what’s the hustle story you pride yourself on?
0:02:03 Well, the first big break was getting into the business.
0:02:09 Art Laffer, I’m not sure if you know Laffer, Laffer Curve, Supply Side Economics, Reaganomics.
0:02:13 He was my professor at the University of Southern California.
0:02:15 He was like an advisor to presidents, right?
0:02:23 Oh, every president since Richard Nixon, except for presidents Obama and Biden.
0:02:26 And, you know, he was agnostic.
0:02:35 If anyone, didn’t matter what party, wanted to hear what he had to say about taxes, deregulation,
0:02:41 monetary policy, he wanted to give his point of view.
0:02:50 And, you know, we’ve come full circle, Art and I, because I and my team introduced Art
0:02:52 in 2015 to Bitcoin.
0:03:00 And when he read our paper, he said, this is what I’ve been waiting for since the U.S.
0:03:07 closed the gold window in 1971, a global rules-based monetary system.
0:03:14 Wrong rule, quantity theory of money, you know, limited to 21 million units, but we’ll
0:03:15 get there.
0:03:18 And, of course, he was talking about stable coins.
0:03:24 So now we have introduced him to stable coins, tether, circle, and so forth.
0:03:26 And he said, ah, the right rule.
0:03:31 Have you seen this website, WTF happened in 1971?
0:03:33 It’s amazing.
0:03:36 There’s an entire website basically saying, what the F happened in 1971?
0:03:42 And it shows like a series of charts where something happened in 1971 and the world was never really
0:03:43 never the same.
0:03:45 And it’s just a, it’s a very compelling case.
0:03:46 It makes you want to go look at it.
0:03:49 And obviously, I think that’s the year that we went off the gold standard, right?
0:03:55 It’s the year we went off the gold standard and all hell broke loose in monetary policy.
0:03:57 We went into massive inflation.
0:04:04 So anyway, it was in the late 70s that while I was in his class that Art introduced me to
0:04:05 Capital Group.
0:04:07 I walked into Capital Group.
0:04:09 I didn’t even know what the investment business was.
0:04:11 I had been a waitress.
0:04:16 I was interested in economics, but I didn’t know this business.
0:04:22 And Capital was the premier firm in Southern California at the time.
0:04:24 Sounds like that might be a tough job to get.
0:04:32 Art recommended me highly to Don Conlon, who was the chief economist of Capital Group.
0:04:42 And I walked in there and Don was losing a person who was going on to Harvard Business School.
0:04:45 So this woman, her name was Claudia Huntington.
0:04:48 She was so good at what she did.
0:04:52 He was looking for one and a half people to replace her.
0:04:55 I was the half, but I didn’t want to be the half.
0:04:57 I wanted to be the one and a half.
0:05:01 So let me ask you a question about that, because I think everybody in their career, you know,
0:05:02 will have an opportunity.
0:05:05 I had one when I moved to San Francisco when I was 24.
0:05:07 I didn’t know anybody, but I wanted to be an entrepreneur.
0:05:08 I wanted to be in Silicon Valley.
0:05:11 This billionaire was hiring for this role.
0:05:12 I don’t know how I got the job.
0:05:15 I was, they literally told me, you’re not qualified for this, but we like you.
0:05:16 We’ll bring you on.
0:05:19 We’ll still hire somebody else qualified for that role, but we want you here anyway.
0:05:23 So I had like my foot in the door and I think everybody has this opportunity to work hard,
0:05:25 but it’s, there’s one thing to put in hours.
0:05:29 So I think there’s a lesson in like first one to be there, last one to leave, like put in
0:05:30 a sheer number of hours.
0:05:36 But what else goes into kind of like making an impression during that like sprint phase of
0:05:38 your career when you can just like fully go full force?
0:05:42 What else besides sitting in the chair for a lot of hours matters?
0:05:44 Do you think, you know, what’s the mindset?
0:05:54 Sitting in the chair maybe matters, but I think the most important thing is, and my objective
0:05:58 was to bring new technology into the firm.
0:06:01 I was using economics time-sharing system.
0:06:02 We were back in time-sharing.
0:06:06 All the charts that you just brought up, boom, boom, boom, boom.
0:06:15 Back then, each one would have cost, in today’s dollars, $5,000 to $10,000.
0:06:18 So that just gives you a sense of how far we’ve come.
0:06:26 But sparingly, I was able to use charts for, you know, ones we made up, so original, and
0:06:34 then, you know, call them from people we trusted and really develop little economic books for
0:06:38 and presentations for Don to use.
0:06:42 Yeah, you’ll get what you want when you help other people get what they want.
0:06:45 So the fastest way to getting what you want is just to give other people what they want.
0:06:49 And I like what you’re pointing out, which is that as a young person, you’re coming in
0:06:54 without the experience, without the network, without maybe the track record or any of those
0:06:54 things.
0:06:59 Those are your disadvantages, but maybe your advantage is tech and new things might be easier
0:07:02 for you to pick up because you have time and maybe you grew up with those tools and you’re
0:07:03 less set in old ways.
0:07:06 And so you bring something to the table and that can be your thing.
0:07:11 Hey, real quick, our sponsor for today, HubSpot, actually did something pretty cool.
0:07:15 If you like money stuff like this, you like investing wisdom like Warren Buffett,
0:07:21 or Monish Pabrai, well, they actually put together a nine investment principles document.
0:07:25 There’s a free document you can have of frameworks that they shared when they came on the podcast.
0:07:27 You can get it right now.
0:07:31 It’s actually just a mental model that separates, I guess, the elite investors from the average
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0:07:35 All right, let me get back to the episode.
0:07:40 I’m just curious, like what is a day in the life of Cathie Wood look like?
0:07:45 What’s your actual day-to-day main thing that you focus on?
0:07:51 I hold sacred in terms of from the moment I get up in the morning until 1030.
0:07:56 That time is all about research.
0:08:04 And so we have our research meeting from nine in the morning to 1030.
0:08:13 First half of it is just the entire research team together and investment team, portfolio
0:08:18 management teams together, really sharing information.
0:08:24 And then we focus on, in the last half hour, on one of the four teams.
0:08:36 So we’re broken up into autonomous technology and robotics team, AI and cloud, which has forked
0:08:43 another team, which is consumer internet and fintech.
0:08:50 Then we have our multi-omics team, which is really all about life sciences and how profoundly
0:08:52 AI is going to transform healthcare.
0:08:55 And I think that’s the most inefficiently priced part of the market.
0:08:59 And then we have our blockchain technology team.
0:09:06 On Fridays at 1030, we have a brainstorm.
0:09:11 And the brainstorm is all our teams coming together or staying together.
0:09:19 But we have another, I’m going to say, another 40 people who have followed us over the years
0:09:21 and are passionate about innovation.
0:09:25 And we invite them to what’s called a brainstorm.
0:09:31 And that is where we try to get out of this not invented here.
0:09:33 We really want pushback.
0:09:41 These are venture capitalists, they’re entrepreneurs, they’re retired engineers, they’re retired professors,
0:09:46 they’re people teaching in universities today.
0:09:54 And they are very vocal because they’re every, all of us, they probably more for their personal
0:09:58 accounts, but we’re all trying to figure out how the world is going to work.
0:10:04 And we’re trying to push the frontiers of knowledge forward as fast as we can and anticipate what
0:10:10 the next set of topics are going to be that people are discussing and trying to figure out
0:10:12 where we should position ourselves.
0:10:13 That’s pretty interesting.
0:10:14 Is that common?
0:10:19 Do you do other firms do this kind of Friday open door brainstorm with like external folks?
0:10:20 That sounds pretty unique.
0:10:22 No, they don’t.
0:10:28 And I’ve done this since 2001 when I was at my last firm.
0:10:35 I just thought it was really important not to get stuck in our own research, but to have
0:10:36 it battle tested.
0:10:44 And we took that to another level when I founded ARC with this notion that we’re going to give
0:10:45 our research away.
0:10:50 We’re going to give our research away, not when it’s finished, because it’s never finished,
0:10:54 but as it is evolving and we push it out.
0:11:00 Now, at the time, 2014, Twitter was for tweens, teens, and celebrities, right?
0:11:04 So I didn’t think that was going to be our primary social network.
0:11:07 We thought maybe LinkedIn would be.
0:11:13 Instead, X has become the most important social network, even for crypto.
0:11:17 I thought Telegram was where all of that was going to live.
0:11:23 And yeah, there are all kinds of conversations, but the ones that we need and that I need to
0:11:24 see, they are on X.
0:11:30 And sometimes we stir the pot, you know, with our research and get debates going.
0:11:36 So I feel that the world is moving so quickly today.
0:11:39 It’s not like it was in 1977.
0:11:49 Back in 1977, as I described, it was really expensive to get information and to travel places
0:11:52 to pull information from management.
0:11:58 And so research departments like the one at Capitol, they were closed and that was their
0:11:59 secret sauce.
0:12:04 Today, information is ubiquitous and it is all over the place.
0:12:07 In fact, you have to figure out, is it real or fake?
0:12:10 You know, so, you know, so that takes another skill.
0:12:21 So I thought, you know, the closed world is probably not where we are best suited for what we want
0:12:28 to do, and that is focus exclusively on technologically enabled disruptive innovation.
0:12:30 That’s all we want to do.
0:12:36 Well, there’s so much information out there and we knew we could harness it.
0:12:44 And it’s how you put it together and what you place priorities on in terms of the kind of
0:12:49 information and the kinds of assumptions that you’re making that become more important.
0:12:56 And you, this might be a dumb question, but like, you will go on TV and you’ll say, I
0:13:00 think Tesla’s going to $2,000 a share or whatever your target price is.
0:13:03 And everyone says, oh my gosh, that’s really bullish.
0:13:07 And you say, here’s why we believe, here’s what, you know, here’s what we believe the future
0:13:07 looks like.
0:13:10 And I hold Tesla and I hope that that all comes true.
0:13:12 But you are very active.
0:13:14 Like you’re buying and selling Tesla all the time.
0:13:18 I looked in the last like 24 hours, your firm has made like 20 trades or something like
0:13:18 that.
0:13:21 Like millions of dollars in and out of these positions.
0:13:25 If you believe Tesla’s going to, you know, some $2,000 a share.
0:13:28 Why don’t you just buy it and hold it?
0:13:30 What is all the active trading for?
0:13:32 And like, are you day trading?
0:13:36 I mean, I’m not from the investment world, so I’m trying to understand, you know, you have
0:13:39 sort of the Buffett mentality and then, you know, you’re very, very active.
0:13:40 I don’t really get that.
0:13:43 Yeah, that’s another great question.
0:13:45 I know it must seem confusing.
0:13:54 So, and we often do describe ourselves, a few people believe this, but we believe it, as
0:13:59 a deep value manager like a Warren Buffett, if you give us five years.
0:14:08 And, you know, Warren Buffett, he was the first to admit, I don’t invest in technology.
0:14:09 He made a few good ones.
0:14:10 Like Apple was great.
0:14:17 IBM not so, but he knew where his strengths were, or he knows where his strengths are.
0:14:18 He’s still with us.
0:14:26 And he did not feel that technology was where he had an edge.
0:14:28 That’s where we do have our edge.
0:14:33 And so, you can say we’re a great complement to the Warren Buffett strategy if you give us
0:14:35 a five-year investment time horizon.
0:14:38 So, why do we trade so much?
0:14:47 Well, because of what has happened to the markets, and really since I got into the business, I think
0:14:54 more than 75% of the trading is algorithmic and high-frequency trading.
0:15:01 There’s a huge amount of volatility in the market itself, but especially in our stocks.
0:15:09 So, if you look at our trading in Tesla, we are using it, we’re using the volatility to our advantage.
0:15:17 So, rarely has Tesla dropped below the number one position in our flagship portfolio, ARKK.
0:15:29 What has happened, it has gone from $100 to $500 and becomes, you know, 13%, 14% of the portfolio.
0:15:37 So, from a portfolio management point of view, we are effectively rebalancing.
0:15:40 That’s a lot of hard work, $100 to $500.
0:15:46 And we know, we know, we know, we know, we know, Tesla is a controversial stock.
0:15:50 Elon Musk is a controversial individual.
0:15:56 And we are going to have opportunities at lower prices to move back in.
0:16:02 And so, that’s the kind of trading that you see around our high-conviction stocks.
0:16:03 Okay.
0:16:04 So, let’s take what you just said.
0:16:07 So, you said, give us five years, right?
0:16:12 Because we’re betting on these sort of long-term technology S-curves that we think are playing out.
0:16:14 So, here’s the tough question.
0:16:22 Over the last 10 years, you’ve been making hundreds of millions in fees, but haven’t outperformed the simple index like QQQ.
0:16:24 Do you think that’s a fair criticism?
0:16:28 So, can I give you a reset here?
0:16:29 I love the question.
0:16:37 You’re giving me an opportunity to answer a question that I know is on many people’s minds, even if they don’t ask it.
0:16:37 So, thank you.
0:16:53 So, our objective as a firm is to deliver a minimum 15% compound annual rate of return over five years.
0:16:55 So, you are absolutely right.
0:16:57 We have not done that.
0:17:00 We have done that since inception.
0:17:07 So, since inception, our compound annual rate of return is over 15%.
0:17:11 Now, what happened in the middle there?
0:17:17 Because you’re focused on endpoint sensitivity, and I understand why people use five years, ten years, all of that.
0:17:17 Sure.
0:17:29 If you use ten years, and you look at Morningstar, and just their quantitative metrics, which have no human input.
0:17:31 They just have their rules-based system.
0:17:36 Based on the benchmark they selected for us, we didn’t choose the benchmark.
0:17:38 We are benchmark agnostic.
0:17:45 We are in the fourth percentile of performance for that benchmark.
0:17:48 Now, that benchmark is mid-cap growth.
0:17:49 Right.
0:18:00 Which kind of fits, because we consider ourselves all-cap, but, you know, if you average, you’ll get mid-ish cap, let’s say.
0:18:02 So, that’s good.
0:18:06 You know, that’s actually saying something.
0:18:17 The space we’ve been in, anything less than large-cap, and especially mega-cap growth, especially in the tech space, has been very tough.
0:18:20 Okay, so that’s another marker.
0:18:23 Now, what about the last five years?
0:18:31 Well, we had COVID in 2020, because this is when we blew up.
0:18:42 I’m not sure if you know this part of the history, but because we were the only investment firm putting our research out on social media,
0:18:53 and the only one posting our trades every day, we went viral during COVID, because everyone was sitting in front of their computers trying to figure out what to do with their time, right?
0:18:55 And their extra money, by the way.
0:18:57 Yeah, and their extra money.
0:19:09 We were actually really one of the few out there teaching people about investing, bringing them along on our journey.
0:19:13 In 2020, we were up 150%.
0:19:17 And at the end of that year, remember, we’re five years away from that.
0:19:19 This is what we’re comparing against.
0:19:24 At the end of that year, I was on Eric Schatzker’s show on Bloomberg.
0:19:29 It was a holiday show, and they gave us a lot of time.
0:19:34 And one of my main messages was, hey, keep some powder dry.
0:19:40 This, we know what goes up like this is going to come down.
0:19:47 It’s just too much capital chasing the opportunity, perhaps too soon.
0:19:56 And that last point, I probably should have said more loudly to myself and to our team.
0:19:57 Right.
0:20:11 Because even though our modeling stock by stock got us to a 15% compound annual rate of return over the next five years, which was very low.
0:20:18 Normally, we’re expecting 25% to 40% compound annual rate of return.
0:20:23 So it had dropped because of the appreciation in stocks to 15%.
0:20:32 But what also had happened and what we did not appreciate enough was many people think, oh, the interest rate increase.
0:20:34 That wasn’t as much the problem.
0:20:39 The problem was supply chain bottlenecks.
0:20:44 Our models are driven by unit growth.
0:20:54 And when there’s an interruption in unit growth, our model, the rate of return expectations come down.
0:21:05 And I thought and we thought we were going to come out of this crisis in a V-shaped recovery.
0:21:06 And we did.
0:21:07 That was correct.
0:21:15 But we didn’t catch how long it was going to take supply chains to reorient.
0:21:21 And that, I think, was a big, big lesson for us.
0:21:36 If I had just focused on that one variable, I would have said, all right, let’s move more into larger cap tech stocks with a big cash position that are innovating.
0:21:39 And it would have been the MAG-6 and all of that.
0:21:41 We did not do that.
0:21:43 What we did, we owned them.
0:21:55 And because we had started doing that in the bull market, we always diversify as a bull market extends because our stocks do tend to go crazy to the upside.
0:21:56 So we were already doing that.
0:22:08 But then in 21, those stocks kept going up and our stocks, so smaller cap and mid cap stocks, started going down.
0:22:11 And so what we always do is rebalance.
0:22:12 We took profits there.
0:22:13 We bought.
0:22:15 That was just way too soon.
0:22:21 It worked out, and I think history will show that everything is fine.
0:22:23 We had to have people stick with us.
0:22:34 And there are so many people who piled in at the top, even though we’re saying, hold your horses, and who left us at the bottom, which is classic.
0:22:36 It’s classic.
0:22:54 And so we’re going to be out there in this cycle a lot more saying along the way, rebalance, sell, take profits, so that when our strategies go through a weak spot, a sinking spell, then you’ll have the psychological wherewithal to buy.
0:22:58 It’s called rebalancing, and it’s a basic investment concept.
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0:23:27 Betting against you as betting against a combination of things that I would never want to bet against.
0:23:28 It’s betting against AI.
0:23:30 It’s betting against crypto.
0:23:31 It’s betting against innovation.
0:23:32 And it’s betting against Elon Musk.
0:23:36 Those are just, that’s not who I, you know, that’s like the Monstars in Space Jam.
0:23:38 I’m just not trying to bet against that.
0:23:43 You might even be right on some, on any one of those individual things, maybe for a period of time.
0:23:45 It’s just not where I would want to be positioned against long-term.
0:23:52 At the same time, like, I’m a normal person, and it’s pretty crazy when, like, this is invention.
0:23:54 In venture capital, too, by the way.
0:23:57 In venture capital, VCs, it’s a rigged game.
0:24:00 I heard this a long time ago, and it never left me, which is venture capital is a rigged game.
0:24:05 You make 2% annually on your fees, regardless of whether you make money or lose money.
0:24:07 And I think what you do is very similar, right?
0:24:13 Like, if you have, like, I don’t know, let’s just use a round number, $20 billion in assets across your ETFs.
0:24:14 Is that about right?
0:24:16 Across the company, we’re closing in on $40 billion.
0:24:20 Okay, that includes the digital assets, private funds, everything?
0:24:24 Yes, and so we do have a venture fund, so I know what you’re talking about.
0:24:27 We’re doing ours a little differently.
0:24:37 We don’t have a carry, so that anyone with $500 can get onto the cap tables of SpaceX, OpenAI, Neuralink, and so forth.
0:24:40 You don’t have carry, so how do you make money in that, just on the fees?
0:24:43 So what we do is we do have a high fee.
0:24:48 So I think it’s 2.75%.
0:25:04 And what we did to arrive there is we said, okay, what do the best venture capital firms in the world deliver over time in terms of compound annual rate of return for their clients?
0:25:08 And how much of the benefit do they derive?
0:25:23 And the best ones, and maybe they’re going to be north of 2.75% per year on average, and especially in this kind of a market where AI is just out of sight.
0:25:35 But the best ones, if you do a very long-term historical retrospective, the historical is retrospective, 2.75% was the landing point.
0:25:38 But we are offering direct-to-cap table.
0:25:43 These are not SPV, not SPVs, so there are no fees upon fees upon fees.
0:26:03 Right, and so I think that the challenge is basically on $20 or $40 billion in total assets, there’s a guarantee, this is why finance and fund management is one of the best businesses in the world, is you get hundreds of millions in fees guaranteed regardless of whether you’re up, whether you’re down.
0:26:07 And then the more you go up, maybe you have additional carrier.
0:26:10 There’s other things in venture capital that you benefit from.
0:26:13 And so I think that’s the challenge, right?
0:26:15 Like Munger used to say, you know, show me your incentive, I’ll show you your outcome.
0:26:22 It’s like, I think all finance and fund management is generally suited towards grow assets under management.
0:26:26 We make money either way, and then like try to do your best in terms of performance.
0:26:30 And in the long run, you know, you will be judged on the performance, but in the short run, it’s hard to tell, right?
0:26:41 Like what’s working, what’s not, which is why when Buffett, I think, started, he basically said, I’ll take nothing for the first 6%, which I think was like kind of the sort of the index standard at the time.
0:26:46 And he said, but if I beat market, then I want 25% of profits.
0:26:49 And I thought like, you know, that was a great structure.
0:26:53 And I think the world of finance has moved away from that because why would you if you could – I would do it too.
0:26:54 If I could take the guaranteed fees, I’m going to take it.
0:27:05 Well, you know, it’s interesting from the – I’m going to say from the 80s on, when I saw hedge fund structures and venture capital, I said, I’m an economist.
0:27:08 I said, oh, that game’s going to end.
0:27:16 And that’s, you know, that – those kinds of excesses, excessive returns, shall we say, they go away with competition.
0:27:27 But in the venture world, and there is a huge amount of competition, but if you look at where the real money is made, it’s in the top, you know, 10.
0:27:33 The top 10 is where a disproportionate amount of the returns are.
0:27:35 And, of course, that’s what we’re aspiring to.
0:27:37 And, of course, everyone is aspiring to it.
0:27:46 But there’s some kind of network effect, and I think it has to do with the network effect is not a viral app.
0:27:47 It is the community.
0:27:58 Well, venture has one different property than what you do outside of your venture stuff, which is in startups, it’s the only asset class where the security selects the investor.
0:28:04 So, you know, for Buffett or any public stock market, I get to just pick what I want to be in, and I push a button, I’m in the stock.
0:28:11 Whereas in venture, the hot startups want to be with the hot funds, and only the hot funds get to be on the cap table.
0:28:14 So the security selects the investor, not just the investor selecting the security.
0:28:16 So it has this, like, that’s where the network effect comes in.
0:28:25 That’s where the brand effects come in, and that’s why Sequoia and Benchmark and these other guys will keep showing up in the top because if I’m one of the top startups, I want them.
0:28:27 And so they get the access.
0:28:31 Even if another investor was totally right in their thesis, they just can’t get in the cap table.
0:28:35 Yeah, it’s a self—yes, there is self-selection.
0:28:39 You’re seeing in the hedge fund world big changes, though.
0:28:46 In that world, the fee structure is changing because passive, the indexes, were outperforming active.
0:28:50 It was a self-fulfilling prophecy because the pendulum was swinging there.
0:29:00 I believe that that pendulum swing, I think, I think that, and consider the source, right?
0:29:11 But I think the pendulum swing, the final swoosh in that direction, was in the last few years towards the Mag-6.
0:29:21 And now they’re so—one of the reasons they’re such concentrated parts of the—now, are they all going to benefit from all of these new technologies?
0:29:26 You know, our focus on robotics, energy storage, AI, blockchain technology, and multiomics?
0:29:28 Some of them will.
0:29:30 Apple, we’ve been watching for a long time.
0:29:31 Finally, it’s out.
0:29:33 They don’t know what they’re doing in AI.
0:29:36 Now I think they’re scrambling a bit.
0:29:38 So, you know, we’ll see what happens.
0:29:41 Each one of them has a weakness.
0:29:44 Each of the Mag-6 has a weak spot.
0:29:54 Sure, they’ll participate in the wave, but they also have some weaknesses caused by the disruptions associated with these new ways of doing things.
0:29:59 And I think we’re at the beginning of that pendulum swing in the other direction.
0:30:01 As I just said, consider the source.
0:30:05 That would be great for us because we don’t own the Mag-6 in our top 10.
0:30:10 It’s not like we won’t own them, but we don’t own them for the most part in the top 10.
0:30:20 If I’m a believer in AI, what’s the number one stock that I should own to benefit from the oncoming AI wave?
0:30:27 Well, I think everyone knows about NVIDIA.
0:30:31 We always try and answer that question with stocks.
0:30:37 People, either they don’t know or they’re not quite thinking about them in the right way.
0:30:39 Yeah, misunderstood.
0:30:42 Maybe not unknown, but misunderstood or mispriced.
0:30:43 So, yes.
0:31:01 As we were selling NVIDIA and we got all kinds of flack, nobody bothered to notice that we put it in the portfolio in 2014 because of autonomous driving at, I think, 20 cents on the current stock’s basis at 20 cents per share.
0:31:05 And we held it for years and we held it for years and no one would listen to us.
0:31:06 No one.
0:31:13 I talked about robotics, talked about autonomous driving, talked about, nope, it was a PC gaming chip company and that’s all it was.
0:31:24 And then it explodes with ChatGPT and, you know, we start selling it and we sold it too soon in the flagship.
0:31:26 I mean, meaning we exited it.
0:31:29 We’re back in it now when it dropped during tariff turmoil.
0:31:39 In portfolio management, you have to not look at just what was sold, but what did you do with the proceeds?
0:31:45 How about Palantir, which I think from that point has done better than NVIDIA?
0:31:48 I don’t know, it was, that was the case at one point.
0:31:51 How about Coinbase when the SEC was suing it?
0:31:55 That’s one of the, that’s what we use some of the NVIDIA for.
0:32:00 It has done pretty darn well, I think almost as well as NVIDIA.
0:32:01 So you have to do it.
0:32:07 So today, of course, NVIDIA, I mean, it still has a very important role.
0:32:10 Palantir still has a very important role.
0:32:14 It is the premier platform as a service company.
0:32:18 We think embodied AI is underappreciated.
0:32:18 What is that?
0:32:22 Embodied AI is physical AI, physical and digital worlds meeting.
0:32:24 You know what I’m going to say next.
0:32:29 Tesla is the largest AI project on earth.
0:32:32 And it’s not just robo-taxis anymore.
0:32:34 It is humanoid robots.
0:32:36 It is humanoid robots.
0:32:43 And according to our research, while the robo-taxi opportunity globally for everyone, including China,
0:32:51 is an $8 to $10 trillion revenue opportunity in the next 5 to 10 years from maybe a billion now.
0:32:52 So think about that.
0:32:59 A billion to $8 to $10 trillion, the whole ecosystem, with the platform companies like Tesla getting half of that.
0:33:01 So that’s $4 to $4 to $5 trillion.
0:33:03 That’s a big market.
0:33:18 According to our estimates, the humanoid robot market will be a $26 trillion market in the next, I’ll say, 7 to 15 years.
0:33:24 So, me and Tyler, the CEO of Beehive, came up with a little challenge for you.
0:33:26 It’s the newsletter challenge.
0:33:28 Now, if you know me, you know that I’m a big fan of newsletters.
0:33:29 I got my own newsletter.
0:33:32 I also had a business that was a newsletter business that was amazing.
0:33:33 I wrote this newsletter about crypto.
0:33:36 We grew it to a quarter million subscribers.
0:33:38 And we ended up selling it after a year for millions of dollars.
0:33:40 And I want you to be able to do the same thing in your business.
0:33:42 So, we’re doing a challenge.
0:33:43 $10,000 is on the line.
0:33:46 Plus, me and Tyler will actually be in your corner as growth advisors.
0:33:49 You just need to go to beehive.com slash MFM.
0:33:52 And you either start a new newsletter or you move your current newsletter over there.
0:33:54 And five finalists will get picked to pitch.
0:33:55 Me and Tyler, sort of like Shark Tank.
0:33:57 And the winner gets $10,000.
0:33:59 So, go to beehive.com slash MFM.
0:34:02 That’s beehive.com slash MFM to enter the challenge today.
0:34:07 I wanted to ask you about this because you put out this great deck or ARC put out a great deck.
0:34:10 And I love this slide.
0:34:12 So, if you’re on YouTube, you’ll be able to see this.
0:34:14 If you’re on audio, sorry.
0:34:16 You know, go to YouTube or Spotify and check this out.
0:34:19 So, on this slide, I’ll just describe it.
0:34:21 So, it’s basically the cost per mile.
0:34:25 Like, how much does it cost to travel, to transport a human being one mile?
0:34:29 And you started like in the 1800s, like horse and carriage.
0:34:31 And, you know, adjusted for inflation and all that.
0:34:36 It looks like it’s $2.10 to travel a mile when you’re on horse and carriage.
0:34:40 Then, you know, you get the Ford, you know, Henry Ford era.
0:34:42 And you’re at $1.10.
0:34:47 And basically, for like, I don’t know, almost 100 years, it’s been roughly the same number.
0:34:49 It’s been $1.10 to travel a mile.
0:34:59 And then your estimate is that with self-driving cars, where you don’t have a driver in there and you’re on an electric self-driving car, that the cost per mile could drop to, your estimate, is a quarter.
0:35:05 So, you know, four times cheaper than what it currently costs and what it has cost for the last 100 years.
0:35:06 Did I summarize your slide correctly?
0:35:08 That is correct.
0:35:09 That is correct.
0:35:14 And, you know, when we first did this research, we too were astonished with that.
0:35:14 Wait a minute.
0:35:16 It cost the same?
0:35:17 Inflation adjusted?
0:35:26 And then one of the reasons for that is because the automobile matured fairly quickly, right?
0:35:28 And we’re all about Wright’s law.
0:35:32 Wright’s law tries to understand, okay, you’ve got this new technology.
0:35:35 You’re starting from a low base.
0:35:47 For every cumulative doubling in that base, so from one to two, two to four, four to eight, for every cumulative doubling, cost decline at a consistent percentage rate for each technology.
0:35:51 Well, the internal combustion engine is mature.
0:36:07 And so it has no shot against EVs, even though I know that’s not the prevailing wisdom in this political climate, or I’m just using economics and learning curves, so technology.
0:36:09 Sorry, what do you mean by it has no shot?
0:36:11 You mean, like, no shot in what sense?
0:36:13 The cost comparison or just—
0:36:16 Nope, because of the chart you just showed.
0:36:19 You can’t get that cost down any lower.
0:36:23 Heck, there are no more cumulative doublings.
0:36:24 Everybody’s got one.
0:36:27 You know, it’s a bit of an exaggeration in the emerging markets.
0:36:34 They don’t, but they’re not going to be paying up for internal—they’re going to be looking for the cheapest solutions to cars, and those are going to be electric.
0:36:42 And the part that I didn’t get was—so, okay, great, the cost is going to go down because it’s a self-driving electric vehicle.
0:36:43 Okay, I get that.
0:36:45 I could see why the cost goes down.
0:36:48 And I assume when the cost goes down, the demand goes up.
0:36:50 It’s cheaper to travel, more people travel.
0:36:54 It gets selected over all the other more expensive ways to travel.
0:37:04 But the estimate you have, where it’s like the cyber taxi revenue, I think you have the autonomous revenue is sort of like in the—what did you say?
0:37:05 You said $10 trillion or something like that?
0:37:05 $10 trillion.
0:37:06 Is it global?
0:37:06 Yeah, yeah.
0:37:17 So right now, if I just take Uber, Lyft, DoorDash, like kind of the revenue of all those companies, which today—I would say ride-sharing is not like a new idea.
0:37:18 It’s pretty prevalent.
0:37:20 I think Uber’s at $40 billion.
0:37:22 You add Lyft, that’s another $6 billion.
0:37:24 And then DoorDash, it does about $10 billion.
0:37:29 So like the total of all three of those companies is only in the like $50, $60 billion range.
0:37:31 But you’re saying that the—
0:37:34 But that is even different from what we’re talking about here.
0:37:41 They are not autonomous, and they are not in the pole position—I mean, DoorDash will harness autonomous.
0:37:49 That’s a very interesting one because we think delivery is with—especially with drones and rolling robots and everything is a very interesting use case.
0:37:53 But Uber and Lyft are not in the pole position for this new world.
0:37:54 Right, right.
0:38:01 But I guess what I’m saying is that’s what’s spent today on taking rides from a ride—like a push-a-button-get-a-ride service.
0:38:03 Oh, I see what you’re saying.
0:38:07 Why is self-driving going to be 20 times more revenue generated?
0:38:11 Why is it going to be $10 trillion when all of those add up to $60 billion?
0:38:11 Right.
0:38:23 What we’re doing is moving from a very narrow subset of transportation called ride-hail today to all of transportation, right?
0:38:29 So we’re moving the entire market to autonomous to get that number.
0:38:33 They’re a very small slice, very, very, very small slice.
0:38:49 And in fact, what’s so interesting in San Francisco, I think that Waymo, we’re finding, research is showing, that people are willing to wait longer and pay more for a Waymo than for an Uber or Lyft.
0:39:12 And I believe this has already happened, the number of miles, even though in San Francisco Waymo is geofenced and Lyft is not, the number of miles in San Francisco, the San Francisco metropolitan area, that Waymo is driving per day has surpassed Lyft and is heading for Uber.
0:39:13 Isn’t that remarkable?
0:39:15 People are willing to pay up.
0:39:18 Now, that’s not in our $8 to $10 trillion.
0:39:32 We assume that, sure, they’ll start maybe right at or below the prevailing prices for Uber and Lyft, but they will drop over time to that $0.25.
0:39:47 So starting in the $2, and it’s surge pricing, you know, it can be $8 per mile, starting $2-ish, $2.50 maybe, and dropping to $0.25.
0:39:50 $0.25 is at scale, right?
0:39:52 So that’s the $8 to $10 trillion.
0:39:55 And, you know, just think about it.
0:40:07 I mean, I would prefer to take an Uber today, even though I have two Teslas, and I love them, but I still have to pay some attention on the road, right?
0:40:08 Right.
0:40:14 I’m curious, how much of the Tesla market cap, I think Tesla’s $1.3 or $1.4 trillion.
0:40:16 How much of that is Elon?
0:40:25 Meaning, if I took Elon off the company, if Elon went to sleep for the next 20 years, and we weren’t going to have Elon running the company, would you keep your position the same way?
0:40:27 And I guess it’s a pie chart, right?
0:40:31 Of that $1.3 trillion, what do you think that number goes to if there’s no Elon?
0:40:37 Well, if you had asked the question like five years ago, the answer would have been different.
0:40:54 But I think what has happened, and one of the reasons Elon has spent so much time doing other things, some of which people didn’t agree with, is because I think he feels they have pretty much solved the last mile in FSD.
0:41:00 And if they have done that, then they’re going to capture the robo-taxi opportunity.
0:41:02 They’re going to be able to scale.
0:41:14 We would not, however, start incorporating humanoid robots in the way we – we haven’t done much yet in our $2,600 price target, and we’ll update that.
0:41:18 We usually update it each spring for public consumption.
0:41:21 And so we have very little for humanoid.
0:41:34 We’d probably be much less optimistic on humanoid robots, and so we wouldn’t put as much in as we perhaps will with Elon at the helm.
0:41:35 Right.
0:41:36 Wonderful.
0:41:37 Well, Kathy, I appreciate you coming on.
0:41:39 It was fun to hear some of your stories.
0:41:42 It was good to hear your take on some of the tougher questions.
0:41:44 So I appreciate you doing this.
0:41:47 Yeah, thank you, Sean, and thank you for the tougher questions.
0:41:55 They’re important – thank you for giving me a platform on which to answer those questions because it is important.
0:41:56 It is important.
0:41:56 Great.
0:41:57 Well, thank you.
0:41:57 I hope to do it again.
0:42:00 I feel like I can rule the world.
0:42:02 I know I could be what I want to.
0:42:05 I put my all in it like no day’s off.
0:42:07 On the road, let’s travel, never looking back.
0:42:08 All right.
0:42:12 Let’s take a quick break because, as you know, we are on the HubSpot Podcast Network, but we’re not the only ones.
0:42:15 There’s other podcasts on this network, too, and maybe you liked them.
0:42:15 Maybe you should check them out.
0:42:18 One of them that I want to draw your attention to is called Nudge by Phil Agnew.
0:42:29 And whether you’re a marketer or a salesperson and you’re looking for the small changes you could make, the new habits you could do, the small decisions you could make that will make a big difference, that’s what that podcast is all about.
0:42:30 Check it out.
0:42:32 It’s called Nudge, and you can get it wherever you get your podcasts.
Get the free investing playbook to invest like Warren Buffet: https://clickhubspot.com/rme
Episode 760: Shaan Puri ( https://x.com/ShaanVP ) talks to Cathie Wood ( https://x.com/CathieDWood ) about her fund’s performance, her biggest bets on AI, and the most misunderstood stock on earth.
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Show Notes:
(0:00) McDonald’s to Managing Billions
(8:54) A day in the life of Cathie Wood
(17:29) ARK’s Performance Review
(30:00) Cathie’s #1 stock pick
—
Links:
• ARK – https://www.ark-funds.com/
• The MFM Newsletter Challenge – https://www.beehiiv.com/application/mfm
—
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• Shaan’s weekly email – https://www.shaanpuri.com
• Visit https://www.somewhere.com/mfm to hire worldwide talent like Shaan and get $500 off for being an MFM listener. Hire developers, assistants, marketing pros, sales teams and more for 80% less than US equivalents.
• Mercury – Need a bank for your company? Go check out Mercury (mercury.com). Shaan uses it for all of his companies!
Mercury is a financial technology company, not an FDIC-insured bank. Banking services provided by Choice Financial Group, Column, N.A., and Evolve Bank & Trust, Members FDIC
—
Check Out Sam’s Stuff:
• Hampton – https://www.joinhampton.com/
• Ideation Bootcamp – https://www.ideationbootcamp.co/
• Copy That – https://copythat.com
• Hampton Wealth Survey – https://joinhampton.com/wealth
• Sam’s List – http://samslist.co/
My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano
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