We Study Billionaires - The Investor’s Podcast Network - TIP150: Questions from the Audience, Artificial Intelligence (AI), Options Trading, Value Investing, & The Intelligent Investor (Business Podcast)

Starting point is 00:00:00 You're listening to TIP. Hey, how's everyone doing out there this week? We are really excited about our 150th episode of the Investors podcast. This was very exciting for us. For the show this week, Stig and I came up with the two biggest things that we've learned over the last 50 episodes. And we discussed what those learning points were. And we kind of have just a quick discussion.

Starting point is 00:00:24 That takes up about the first half of the episode. And then in the second half of the episode, we cold call two. members of the TIP community and allow them to ask us any questions that they want. And they came up with some fantastic questions that I think that you're really going to learn a lot about. One of the insightful questions was about whether you would prefer a good management but a broken competitive advantage or rather a strong competitive advantage but a bad leadership. Another great discussion is about options and how to use options in the stock market.

Starting point is 00:00:56 perhaps that's a better approach now that the stock market is so high. And from a second caller, we have a really interesting question about how to value the assets on the balance sheet. We also get into whenever Stig and I are talking at the start of the show, we get into a pretty in-depth conversation about artificial intelligence and machine learning, hyperdimensional space, and all sorts of things that were just loads of fun. So if you guys are ready, we're ready, let's do this. You are listening to The Investors Podcast, where we study the financial markets and read the books that influence self-made billionaires the most.

Starting point is 00:01:35 We keep you informed and prepared for the unexpected. All right, how's everyone doing out there? We are really pumped because 150 episodes, you know, for me, Stig, when I think about 150 episodes, it's really hard for me to even imagine us sitting down and seeing face-to-face over Skype and having. these conversations 150 times for almost an hour for each one of those discussions. It's kind of mind-blowing for me, but really exciting at the same time to consider that we've been going through all of this together for so long. Yeah, you know, Preston, I feel the same way. And I feel like it was yesterday that we had the same exact discussion about Epson 100. We were like, what did we find the time to record 100 episodes? And now we're here one year later and it's the

Starting point is 00:02:25 same, right? I know, I know. All right. So here's what is a pretty similar format to what we did show 100. Stig and I are going to talk about the two things that we've really learned since the last time when we did episode 100. Mine are kind of different than what I think people are going to expect. I'm real curious to hear what Stigs are. So Stig, I don't know if you want to kick this off and lead the discussion, but let me hear what your first one is. I'm curious. Yeah, so my first takeaway that is having the right mindset or perhaps I should say choosing the right mindset. And I'm specifically talking about episode 129, where we read the book called Mindset. It really focused on whether or not you have a growth mindset or a fixed mindset.

Starting point is 00:03:09 And people can come back and listen to that episode in detail. But I think it was really interesting to see how the author, Carol Drake, really illustrates how our beliefs about our capabilities really exerts tremendous influence about how we learn and with path we take. life. And I think what I reflected upon afterwards is that you can actually be very successful in life without a growth mindset. I mean, having a fixed mindset, and I'm specifically talking about Steve Case, the AOL founder, and Paul Allen. To me, that seems, at least for a billionaire, seems to have somewhat a fixed mindset. I think it's interesting to see how they've been successful

Starting point is 00:03:49 in business, but perhaps just with one business. Whereas I'm looking to someone, like Reed Hoffman. And we talked about this book in episode 125. But he talks about, you know, PayPal, like successful LinkedIn and now having a successful VC company. And not only in terms of net worth, that's actually not really what I'm talking about, about how he came off as really happy and always learning

Starting point is 00:04:14 and connecting with smarter people and really try to grow that way. Whereas if you read Paul Allen's book, The Idea Man, which is also a really great book, you know, there's a lot about rivalry with Bill Gates and there's a lot of really negative emotions attached to that. And I think a lot of that has to do with which type of mindset we choose to have going on life. So I think if there's a really one takeaway for me, really just to kick off the show, it's about choosing your own mindset.

Starting point is 00:04:44 And by doing so, you will basically balance your financial success with your happiness. So I completely agree with you. I mean, this one's huge. And believe it or not, my first point has somewhat to do with your point, but it's not going to seem very obvious when I first start describing this. So we had an interview where we were talking to Brad Stone, who's this huge writer out in Silicon Valley. He wrote the book on Jeff Bezos. At the end of the interview, and I can't remember how much actually made it into our final cut, but we had a in-depth discussion with Brad about what's coming next in Silicon Valley. And he started talking about artificial intelligence. And he was just telling Stig and I, like, this is a lot of this

Starting point is 00:05:24 is going to be such a game changer that people don't really realize is coming in the future. You know, I really, honestly, I know nothing about artificial intelligence. And so I started doing some research after talking to Brad Stone on why all these billionaires out in Silicon Valley are investing heavily into artificial intelligence. And I guess what's changed that's made this such a big deal? And, you know, through a little bit of research after that show, I came upon a gentleman called DeMise Hacippus. And Demise is the founder of a company called Deep Mind. And DeepMind was recently acquired by Google for something like $700 million or some outrageous number.

Starting point is 00:06:06 And let me just tell a little bit of a story about this gentleman because this stuff is so fascinating. It is just mind-blowing how much this is going to impact you as an individual in the next five years and how much I've just learned through that little quick conversation with Brad Stone. So this story about this Demise Hasippus is fascinating. At the age of four years old, he started learning chess. By the age of like 12 or somewhere around in there, Demise became like a grand master. He's one of the best chess players in the world. And so this guy is wired for like just, you know, he's one of the smartest people around. Well, he goes on and he starts programming. He gets into this programming stuff in his teens. And whenever he was something like 15 years,

Starting point is 00:06:50 years old or somewhere around in that age, he programmed a chess game to play. And he actually, one of the interviews I saw with him, he said that the chess game was so good that it actually beat some of his siblings whenever they played it. Now, for anybody who's ever done any programming work before, programming chess is so hard because there's so many different outcomes that can occur in that game. To the point where I don't know how anybody could even begin to start cracking that code because there's so many different positions and so many different moves that can be made. So he's doing this in his mid-teens. Long story short, by the age of, I think it was 17 years old, he created one of the very first sim games, and the sim game was about creating like an amusement park

Starting point is 00:07:34 where, you know, the player can create rides and then these fictitious people show up. And it was the very first attempt that he had made to use artificial intelligence in this game where the people would show up and they'd bring friends if they liked the amusement park the next day. This thing went on to be a huge hit. He made tons of money with this video game that he programmed. And long story short, he has this history of working with artificial intelligence. Well, he goes off to Harvard. He gets a doctorate in neuroscience and he starts studying the brain and how the brain works and all the neural networks within the brain. And he then goes and found this company called Deep Mind. and what deep mind is all about is using machine learning, artificial intelligence, and what he's done is he's basically mapped out how the human brain works through neural networks and hyperdimensional space.

Starting point is 00:08:29 And he's recreated that into a computer algorithm. Now, I know all this stuff sounds absolutely crazy, but there's a video out there that I watched. And Stig and I are going to put this in the show notes of this episode. And in the video, what he talks about, what the me see. talks about is I wanted to take an approach to artificial intelligence where we basically created the human brain on a computer. And to test that out, what he did is he took all these 1980s video games like Atari and Nintendo games. And the input to the computer was simply a video capture. They hooked up like a video camera to look at a TV screen. And that was the only

Starting point is 00:09:08 input into the computer. So it's looking at the pixels on the screen. And that's the only input that the computer is receiving. And then the computer is told, all right, you have a joystick. You can move left, right, up, down. You got an A and B button or whatever it was. And that's the only thing you can control. Now, once he set up this computer to do this scenario, the computer had no idea how to play the game.

Starting point is 00:09:33 It had no idea what it was doing. All it was doing was looking at the screen and it knew it could control the little player on the screen. And it was told to maximize the score. Look up here in the top left side of the screen. and whatever gives you the highest score, that's what you're trying to maximize. And that's the only thing that the computer was told. So in this video, he shows the iterations of the computer playing the game.

Starting point is 00:09:57 And so the computer's playing in it at first. You ever see the game Pong where there's like a little bar at the bottom and the ball kind of bounces around? It bounces off the side of the screen. So it was playing a game like that. Only at the top of the screen there was these blocks. And the ball would bounce off the bottom of the little bar. that's being controlled going left and right. The ball goes up and it bounces off the blocks and it would break up the blocks as it would do this.

Starting point is 00:10:19 And the goal was to not let the ball drop below the little bar that the person's controlling. So he shows the iterations of this. And the very first iteration, the computer's controlling the little bar at the bottom and it's all over the place. It's not working well at all. You know, it's failing. It's not able to do it. But then he shows, okay, so this is what the game looked like after 50 iterations and the computer's learning how to control these inputs and trying to maximize its score.

Starting point is 00:10:43 And he says, we haven't taught the computer how to play the game at all. We haven't said, if the ball moves here, then do this. There's no if then statements or anything. So I'm sure there's some in the code somewhere. But as far as my technical knowledge of programming, you've got to bear with me here. So after 50 games, he looked at how this thing was performing. And the game was starting to figure out that it needs to move the bar in order to bounce the ball. And it was getting a lot better.

Starting point is 00:11:09 It was still making mistakes, but it was getting a lot better. Then he fast forwards. And he says, this is how it performed after 300 games. And after 300 games, the bar at the bottom that the computer was controlling was not missing this ball that was coming back at all. And it was bouncing up and it was hitting the blocks and destroying the blocks at the top of the screen. And so he says, after 300 games, we had the computer performing just as good as a human. He said, then we got this crazy idea. Like, what would happen if we just let this thing play 600 games?

Starting point is 00:11:39 And so he fast forward. He showed, okay, this is how the computer played the game after 600. rounds of this thing learning. And lo and behold, the game or the computer had figured out that it needed to knock the ball off the left-hand rail of the screen in order to knock all the blocks that are aligned to the left side of the board. And it buried like a hole into the left-hand side. And then the ball got to the top where there was a little gap. And the ball was actually knocking all the blocks out without the computer even controlling the bar at the bottom. It was automatically doing it.

Starting point is 00:12:12 It figured out it needed to burn. a hole on the one side and then the ball would just take care of the rest. And he said, the most fascinating part of all this is he said, our programmers are really good, but our programmers had no idea that this technique could even be done in the game. And so he said, what had happened was we created an algorithm that ended up teaching us how to play the game, which was totally mind-blowing for me. Now, granted, this is just a little 1980s video game. And just so you know, the algorithm was applied to every video game that was loaded into Atari. It wasn't just this one game.

Starting point is 00:12:45 They were able to apply the same algorithm to all the games. Now, this is where my mind basically has exploded recently with where all this is going. So there's a Chinese game called Go. And for anybody that's ever seen this, the board is 49 positions, basically like when you look at a checkers board, it's black and white, black and white. Only in the Chinese game of Go, it's 49 by 49. So it's a huge board. There are more positions that can be played in this game than there are atoms in the entire universe to give you an idea how complex this game can be with respect to how many positions can be played.

Starting point is 00:13:25 And that's not something that I've just made up. That was actually a quote from Demise has been the founder of Deep Mind himself. So in the past, this game has never been beat. The professional, the best players in the world at Go have never been beat by a game. computer ever because it's such a hard programming challenge to program so many different positions. So what does DeepMind do? They take on this challenge. And what they did is they loaded all the past games that have occurred with this Chinese Go into the index so that the computer had a baseline of how it should play the game. And then they did this. This is what I find so fascinating.

Starting point is 00:14:02 They took the computer and they made it play itself to get better at playing the game of Go. They set it against itself, and then they made it play itself like a million times. And every time the game lost to itself, it learned why it had lost, and it got better, and it got better, and it got better. And back in January of 2017, there was a tournament where an individual from China was playing. He was one of the top 30 or something in the world. And he was playing against this unknown competitor, and it was over an online platform. Well, lo and behold, this unknown competitor,

Starting point is 00:14:38 ended up beating him. And he was so devastated that he checked himself into the hospital because he was so mentally fatigued from the situation that he couldn't believe that he lost. And he couldn't believe he lost to somebody that he didn't even know. And there's a very profound point that I'm getting to with all this. So hear me out with the rest of the story. So lo and behold, it was revealed that the person was actually playing Deep Mines Go computer and he had lost. So just recently, I think it was in May of this year of 2017, the Deep Mind Go computer took on the number one player in the world and beat him four out of five games. And they've never programmed this computer on how to play the game.

Starting point is 00:15:22 They've just set it loose on taking the inputs from its environment and continuing to pursue being the best in learning as it goes along. So there's so much more that we could discuss on this topic. I wanted to give you this background, A, because I find it very fascinating, but B, because it actually ties into Stig's point about mindset. So it Sticks talking about mindset and how you have to have a positive mindset. You have to have a growth mindset when you're trying to achieve something and that you do it over and over and over again until you actually figure out all the variables at play and you're actually able to solve the puzzle.

Starting point is 00:15:57 Through artificial intelligence, we can see that the only way that the computers were able to become the best in the world at things that people have dedicated literally their entire lives to and be able to do it in such a short amount of time is because the computer had no choice but to keep a positive mindset when it was learning. When it was playing a million games against itself, it never got discouraged and said, I'm going to quit because I just lost this last game. It didn't have that option. In a way, artificial intelligence is programmed to be a positive mindset at all times and a growth. mindset at all times. And there's so much we can learn from that as human beings. When we sit back

Starting point is 00:16:37 and we look at this, because we can see that what's being preached with some of these books, like the mindset book, is real. And the reason that it's real is that it's 100% provable through artificial learning and machine learning at this point. And that's how these machines are able to achieve at such high levels. Now, where it's going in the future with cars and everything's going to take me to probably be my second point, but we're not going to talk about that right now. But I find this stuff fascinating. I find the fact that if you don't know anything about this, I would tell you to get on YouTube and start watching some videos and start reading and start learning about this because it's going to be a very, very important thing in your

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Starting point is 00:21:25 All right. Back to the show. So, Preston, I mean, what a fascinating story. And I mean, you know me. My first thought is always, how does that tie into stock investing? Because what you said about artificial intelligence, I guess there's some kind of way to program an algorithm just to test a lot of different investments. I don't know if it would just be a mock portfolio or whatnot.

Starting point is 00:21:47 but in terms of how to find the best stocks and perhaps look at key ratios or some more qualitative thing that we have a really hard time quantifying today that artificial intelligence can find. So what are your thoughts on that? I know that you must have thought about that. So that's probably one of the first things I thought about after watching some of this stuff and learning about this stuff. And one of another really interesting videos that I watch that will also drop into the show notes was about how machine, learning is augmenting text. Have you ever filled out a capture code whenever you're applying to a membership on a website or whatever to make sure that you're not a robot? And it gives you like these really strange like letters and numbers that only a human can basically decipher and that a computer can't. Well, with this machine learning, they're actually getting so good that machines can actually decipher that without anyone typing any code about it. It has learned this through itself. And what it's using is a thing called hyperdimensional space. So instead of trying to describe hyperdimensional space to people, A, because I'll probably mess it all up.

Starting point is 00:22:53 But B, there's a video that goes along with this and it makes it so crystal clear for understanding. And we'll drop that into the show notes for people to watch. So what this hyperdimensional space can do is it can figure out words. Like you could load 10 million characters of text into the computer. And the computer is going to go ahead and it's going to look at all this text and it's going to learn which words are associated with what, what that. means you could also provide other inputs like, let's say we were loading up 10Ks and 10 Qs from companies across the entire stock market into this algorithm to learn, this deep mine algorithm.

Starting point is 00:23:30 And so it's learning what words are associated with what. And then what you could also do is another dimension in that hyperdimensional space is you could say this company had a P.E ratio of X at this point in time. This company had a debt to equity of whatever at that exact moment in time. that 10 queue was published. And so those other aspects can then be attached to that single point in space. And so what the machine learning can do is if we then said, let's say we go five years into the future and we say company A, B, and C all went bankrupt.

Starting point is 00:24:04 Now tell us where there were similarities in their 10 Q's or similarities in certain numbers that made these companies kind of stand out. And with machine learning and this deep mind protocol that's using neural networks that are completely shaped after the brain, okay, and the way that the brain learns, you could actually see in 3D, like in this video, it does a great job of showing you in 3D how this looks, but you could actually potentially see where those companies were at with maybe their bond ratings. It really depends on how many variables you want to add in there. The more variables, the better, because this thing, it works better when you add more variables

Starting point is 00:24:45 into it because it can decipher all that stuff out. So I'm thinking of it in terms of rating agencies like Moody's. You have maybe a couple analysts that are looking at things and saying, yeah, I think it's a double A or a single A minus whatever. I think with machine learning, all of that is going to be drastically changed in the future. And I mean, that's just one little aspect of where this could potentially go. I think that there's big, big potential for this stuff changing in the future. Given how digital everything in stock investing is and really all the algorithm that stock traders

Starting point is 00:25:20 are already using on Wall Street right now, it must be a question of a less than a decade before some version of this efficient or not will be taken to use, at least in my opinion. My second point really comes down to something that Stig and I and the rest of the TIP community heard when we were at the Berkshire Hathaway Shareholders meeting. And it totally relates to the artificial intelligence stuff. and that comes down to this idea of productivity and the impact of some of these technologies moving forward with labor and competitiveness for people out there listening to the show. So I was watching a video with Elon Musk and Musk made the comment that 15% of all jobs in the United States are driver-based jobs, whether you're a taxi driver, an Uber driver, a truck driver, somebody driving something, delivering cocaine. or whatever.

Starting point is 00:26:14 That's 15% of all employment inside of the United States today. And Musk said, I can't remember the exact number, but I want to say that he said within 20 years, all of those jobs are pretty much going to be gone. There's going to be no need for those types of jobs anymore. And the argument for him is that with machine learning and artificial intelligence, I mean, get this stick, listen to this. They're saying that with this neural network,

Starting point is 00:26:41 machine learning that with the way that they're programming autonomous cars, they're taking all these inputs, call it LiDAR on the car, they're taking the cameras from the car, they're taking all that information. And those are the inputs and it's able to actually learn so that the more that the car is driving, it almost becomes flawless at driving over an extended period of time if you have a large volume of people that are actually exercising the algorithm. So in a way, they're not even programming the cars to learn this stuff. The cars are learning it themselves by exercising this deep mind protocol that they've set up through machine learning. I find that fascinating. But anyway, back to my point with productivity. So if that's true, and based on the talk I watched with Musk, I mean,

Starting point is 00:27:27 he was, there was no doubt in his mind that this is happening and it's happening within a couple years. He displayed this picture of this large semi-truck that's electrically run, that's completely AI-based driverless technology on the thing. So for me, whenever I think about this, and this is just one aspect of how all this is going to impact jobs in the next 10 years, is just a driver piece of it. I can't imagine where that's going to go in health care, where it's going to go in finance, where it's going to go in all these different directions. So let's just say 15% of the driver part is the only part that we're talking about.

Starting point is 00:28:04 Let's just take a very conservative estimate on what we think the displacement's going to be for labor in the next 10 years. 15% of people are going to have to go out there and they're going to have to find new employment. I guess for me, whenever I'm thinking through all this, I started thinking to myself, well, what's my competitive advantage? What skill set do I have that's going to remain competitive, that somebody's going to be willing to continue to pay me, whether that's a customer or a company or whatever, for what I know

Starting point is 00:28:32 and what I can bring to the table? And how might that be eroded moving forward? And I think that that's such an important question for people out there that are listening to this, that they really need to think about. Because if you're not prepared for this thing, this stuff doesn't care whether you're prepared for this or not. It's coming and it's coming like a freight train. And for all the students out there, you really need to think about how can I design a competitive advantage around my skill set as an individual person that's going to be a contributor to the labor force moving forward. and I think this is a really, really important thing for people to think about. I really like what you said, especially here at the end, Preston, about encouraging students to pinpoint what is really their competitive advantage.

Starting point is 00:29:19 And having hundreds, if not thousands of conversations with my own students, this is something I always go back to one way or the other. Because students would typically like to know how do I get a good grade or what will you ask for the exam. Which is, you mean, obvious questions you will ask if you're a student and always tell them to ask the right questions instead. Why are you not focusing on really understanding what it is and perhaps even challenge me? And sometimes they would say, well, you know, you're giving the grades. So why would I even tell you that you're wrong? Which is just not a good approach because you always need to keep educating yourself and to make yourself irreplaceable. If you are thinking just for the simplicity of this example, like as a student saying, how do I get the best grade?

Starting point is 00:30:04 what is the formula? What do I need to find on Google and copy paste into my exam? If you try to come up with some kind of formula or process that everyone can replicate, then you are just not positioned for the new future that we're going into. So my final thing is something that I learned a lot from. I decided to call this part the simplicity and complexity of the best investors in the world. And here I'm specifically talking about Monis Paprai, who interviewed for episode 120 and 21.

Starting point is 00:30:34 and also Bill Miller. And just a really quick introduction to these two gentlemen. I mean, both of them has really been crushing the S&P 500. Monashap Rai, he's been doing that through his fund, Pop Rai funds, and Bill Miller through Lake Mason Capital Management, where he's been managing more than $75 billion. And what really dawned to me whenever we were talking to these guys was in a way how simple the process were.

Starting point is 00:30:59 So, for instance, Monis Pabri, he said multiple times that he didn't find he was unique, he was actually a cloner. So what he did was he was going through the 13F filings. And the 13F filings is something that all managers, money managers in the US has to file if the man's least $100 million. So he would be saying, I have the best stock screen in the world. I mean, I'll just be looking at super brilliant people and what they're analyzing. And then if there's something that I understand, I might invest in it. And then you have someone like Bill Miller. And what he was saying is that he was looking at. He was looking at. looking at the free cash flow yield. So he was basically saying, where can I get the highest

Starting point is 00:31:38 free cash flow for the least amount of dollars? That was really his starting point. Then he would rank them and then he would go through them one by one and ask, do I understand the business? Can they sustain the cash flow? So they were more or less using their own version of a very simple stock screener method. For me, whenever I'm ranking the cheapest stocks out there, I mean, one of the cheapest stocks that I can look at, that would be Fiat Chrysler, for instance. This is the car industry. I'm not saying it's super complex, but it's definitely not my forte. And then another super cheap stock out there that would be travel insurance.

Starting point is 00:32:13 I mean, I'm definitely not the authority within financial institutions. So, I mean, I will not look at that. Pop rime might, Bill Millamite, but I'm definitely not. For me personally, I would come down to something like Bethbeth Beyond or GameStop. And we just did an episode about those two picks in episode 143. but in any way, that's something that I understand. And I think I have a pretty good idea about the mode and how to value the stock. So I think it really tells you something about in many ways how simple approaches ranking

Starting point is 00:32:45 the stocks, kind of like using a stock screener, if more efficient stock screener perhaps. And then based on that, use your own qualitative skills to value the stock. Like, it's something you understand. And what do you think the value is? So I have a comment about Monish Pabre. So, you know, Stig was mentioning how Monish comes out and says that he's just a shameless cloner and all this stuff. And, you know, I guess to a certain extent, yes, but I think that there's a really profound learning point that I think a lot of people miss whenever he says things like that. So what I think Monish Pabri really is is he's a cloner of a Keystone habit.

Starting point is 00:33:22 So whenever I look at Monish Pabri and the Keystone habit that I'm really referring to is, is that he's a hardcore learner. The guy, if you go to his website and we need to go find the link and we'll put this into the show notes, Monish has a reading list of all the books that he's read. And I've looked at this thing. And I feel like, you know, I feel like I've read quite a few books. And there's definitely people out there that read a whole lot more than I do. But whenever I looked at his list, I was like, there's no way, there's a human being that has read this many books.

Starting point is 00:33:51 I mean, it is an ungodly amount of reading. And so then I started thinking, well, I could only imagine what you. Charlie Munger and Buffett have read in their day because that's where he got this habit from. So when he says he's a cloner, he is a cloner of habits. He's looking at the critical elements that have made Buffett and Munger so successful, which is reading and learning like an absolute maniac. And he's cloned that. Now, there's other aspects of things that he's cloned from Buffett and Munger,

Starting point is 00:34:21 but I would argue that those are much less a factor of him just ripping somebody off of what their idea was. And what it was is he's so well read that he's like, oh, well, this is definitely the best model because of A, B, and C, because I've learned all these other things that teach me that this is probably the best approach to do something. So when Monish says that, I think people need to pay attention to what he's actually talking about, which is he's cloning their habit of doing a lot of hard work and studying his face off because that's the real thing there, folks. It's not that he's just copying stock picks and, you know, modeling his company after Buffett's original partnership, although he did that. I think that that's a lot less important than the other piece.

Starting point is 00:35:08 Yeah, and real thing is a good comment, Preston, because that really tells you something about the simplicity and the complexity about this process. Like, it's really simple to go in and read all the 13 apps and really say, wow, this is, you know, we're the best investors in the world. that's what they're investing actually do the same. And Mitzfabre actually proved that that you can actually beat the market by really decent return just by not thinking and all, but just by copying the best investors in the world with 13Fs filing. But really the complex thing that he's bringing to the table is he's saying, okay, so this is my pool of stocks. Now, I need to use all that I learned from the hundreds of, not thousands of books, to identify

Starting point is 00:35:49 so which stocks should I focus on? You know, that's where the complexity part comes in. because you can't, it's really, really hard to outsource. You really need to learn that yourself. You know, it's amazing, and I hate to bring up the artificial intelligence learning stuff again, but it all ties into this. Because when you look at how this machine learning takes place and it sets up neural networks and how these nodes interact with other nodes as if they're synapses firing within the brain, the more that the artificial intelligence machine learning can basically pack into these different dimensions,

Starting point is 00:36:22 the more it can add to each one of those neural spaces on the hard drive. The more creatively, and I know that that's probably a lot of people might argue this, but you need to go watch these videos on how this individual is being creative and intuitive with machine learning. It really gets quite fascinating and you understand why it's so important to read, not just one topic area, but multiple topic areas because of the way that the brain works and the way that the brain is structured, and the way that your decision-making changes as you learn other aspects

Starting point is 00:36:57 and how those synapses are tied to each other within the brain. So I can't tell you, folks, you need to come to the show notes and check out these videos that we're going to put on here because after you watch these things, your mouth is absolutely going to hit the table as you're watching this, and you're going to have a deep appreciation

Starting point is 00:37:12 for how powerful your brain is. And it really starts making a lot of sense why reading and expanding your knowledge and studying various areas, whether it's art, whether it's science, whether it's all this stuff, but how much better of a decision maker you become when you are doing that. And the person that comes to mind for me is Charlie Munger. In fact, Buffett said that at the last meeting. He says, you know, Charlie's a much smarter person when it comes to understanding just various areas of interest.

Starting point is 00:37:42 He said, we're on much more narrow and focused just on investing and how I can make money for Berkshire Hathaway. And I think, honestly, I think that's one of the reasons he, he's held on to Charlie so closely is because he can tap into Charlie's mind and basically gain that perspective of all these other areas that he doesn't have knowledge in. And it drastically impacts his decision making. And it helps him understand things better because he's able to tap into Charlie's mind. And even though you might look up on Google that his net worth is called $1.4 billion and one profit is in excess of $60 billion, I think in many ways Charlemonger might be an

Starting point is 00:38:19 even better investor, at least in terms of evaluating the mode of different businesses, simply because he's so knowledgeable about so many different things. And he wanted to retire age 40, well, he wanted to retire as soon as possible, but he did that age 42 when he was like multi-millionaire. And the reason why he did that was just to become smarter about the world, not necessarily to make money. Making all that money was just more like a byproduct of him putting some of the things you read into practice and really found a good way through Berksa Heller way to do that. I'm 100% sure that Charlie Munger could have met multiples of the network than he has today if that was his goal.

Starting point is 00:38:56 We were interviewing Robert Chaldeenie, who's one of my favorite authors on the planet. And when we asked Robert what his favorite book was, he said, for Charlie's Almanac. And I have a copy of it at my house. I couldn't agree more. When you read that book, you really, you have a whole different perspective of who Charlie Munger is after you read this book. It is mind-blowing the content in this book. Anyway, so let's get to the fun part of the episode here. We're going to start cold calling members of our audience that, you know, had said they wanted to be contacted.

Starting point is 00:39:27 So we're just going to place some calls here and see what happens. Hey. All right. So today we have Brittany with us. And Brittany, welcome to the call. Thank you. It's very good to be here. So, Brittany, this is your opportunity to ask Stig and I, any question that you want.

Starting point is 00:39:50 How long have you been listening to the show for people just to kind of know when you started? Yeah, probably now about the 16 or 18 months, I'd say. Okay, all right. So you've been around for a little bit. And so what is on your mind? What do you want to hit us with? Well, first I just have a kind of a general question. It's off the wall, not necessarily strictly stock market related.

Starting point is 00:40:11 But more pick your brain. So I know you guys are huge Buffett fans. And, you know, he has strict rules on what he follows as far as good buy. Keep that in mind and I'll ask you this question here. If you had to choose to invest in one of these two scenarios, which would it be in why? And just assume that the business model and the personnel must remain the same. Okay, so it's kind of a riddle. Company A has a great competitive advantage and a mediocre leadership team.

Starting point is 00:40:40 And company B has a mediocre competitive advantage, but a great leadership team. Which would you invest in and why? If it was only kind of those two variables, I'm probably going to take the leadership team every day of the week. And I'd probably put the competitive advantage being mediocre as my second choice. Because I mean, if you have great management, you have creative people, you have something that's a well-oiled machine of leadership. They can create a competitive advantage over time that's not there. I'm curious to hear what Stig thinks. So, yeah, this is a really interesting.

Starting point is 00:41:13 I have a different opinion. And then I also have like a comment. to that, I guess, because Preston, what you were perhaps relating to before was what we talked about with Ed Catmas book, Creativity Inc. where he really talks about it's all about people. You don't need to have a good plan or good strategy as long as you have the good leadership, the right people. They will ultimately come up with what is the right plan for them and then also for the company. But Munger and Buffett had been really vocal on this. And Munger actually said, it's probably a decade ago or something like that, to one of the Berkshire meetings that, if he

Starting point is 00:41:46 to choose between the two, he would take the quality business because the quality business can really carry bad management. And one of the examples that Warren Buffett comes up with is typically, you know, the horrible experiences he had with the taxile industry. You know, he spent 20 years in that industry. Even though that he might be a great manager and could high great managers, the industry was just doomed to be heading for trouble because the commodity prices for the inputs for the company just didn't pan out. And it was just just, a horrible business for so many ways. So I think my take would be the business model. And if I can come up with another example, it would be something like McDonald's. A lot of people don't know this,

Starting point is 00:42:27 but McDonald's really doesn't make that much money on food. They're making a lot of franchise and they're making a lot. They're basically in real estate. That's what they do. So if you in comparison would try to make money just from food, it's actually very, very hard, regardless having the best chefs and regardless having really good leadership in place. So I think that would be my perspective. I see you have something here, Preston. Yeah, so the quote that Stig's referencing,

Starting point is 00:42:53 it goes like this. This is by Buffett. He says, when a management with a reputation for brilliance tackles a business with a reputation for bad economics, it's the reputation of the business, the one with the bad economics,

Starting point is 00:43:04 that remains intact. So I completely agree with that. But again, it goes back to how I kind of opened up with the question. I think that to just say there's two variables really kind of makes us almost

Starting point is 00:43:16 impossible to answer. If the business was Google, and let's say Google had really poor management, but the competitive advantage is that every single brain in the entire U.S. and the global economy for that matter just automatically logs onto the internet and types Google without even thinking. That's a pretty insane competitive advantage that's almost impossible to overcome as far as I'm concerned. So that's a situation where I completely agree with Buffett and Munger, and I think that that quote is going to ring true. There's other businesses out there. And I guess I'm thinking more because, you know, I run a small business. I run a team of just a few people. And I think so much about leadership and making good decisions as a leader without, let's call it, this massive competitive

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Starting point is 00:47:34 Well, you know, Preston, I mean, you are the competitive advantage. So, I mean, I don't think you're wrong here. I mean, if you're a small business owner, the leadership is the competitive advantage because you typically don't, I mean, you don't have a moat. You don't have Google's algorithm or you don't have McDonald's setup. So you are a competitive advantage. So I don't think we are that far away from each other. So I'm more curious, Brittany, why you are interested in that question? You know, I actually have to give credit to that question to Dana because we were chit-chatting back and forth. And I asked her, I said, you know, would you have any questions to ask the guys? And she's a, you know, entrepreneur, her terminal degree is an entrepreneurship and leadership. And so, of course, she went to this sort of realm. So I thought it was a great question. And I honestly thought that you both were going to pick the same answer.

Starting point is 00:48:21 But, you know, this is nice to see that you guys disagree on something else for a change. So it's good. Thank you. For me, I really would have to say, honestly, it really depends. It depends on more variables than just the two. And I think this brings me to another point. So I get really frustrated when people think that they only have two options or when things are binary or even three options in their life. Most of the time, almost all of the time, decisions are so much more complex and have so many more outcomes than what people realize. And I think that's a really important learning point because so often people are presented, well, are you going to do A or are you going to do B? And I think that the response that most people should say is, are those the only options that are available? How about C, D, E, F, and G as other options and what are the outcomes and potential probabilities of all those? And I think that that's something that's really important that a lot of people miss, especially early on when they're starting their own business, is they don't necessarily think outside the box maybe as much as they should. All right, Brittany. So thank you for the awesome question. Do you have another one? I do actually have another one. And this one is in regard to your personal thought process behind reallocating your funds. So as we all know, with everything being overvalued currently,

Starting point is 00:49:43 a lot of people are holding a large cash position. So I'm curious what thought process you go through and what market drivers or driving factors do you look at that will prompt you to put your cash position back to work for you in the market. So that's a hard one. No. I guess for me, it's all about valuation. I mean, when the evaluations are good, I'll be putting money back into the market. I think some people like appreciation of how much the valuation really means for your expected return.

Starting point is 00:50:15 So there are a lot of businesses out there. They're not bad at all. I mean, for years, I would have liked to be invested in Coca-Cola or Disney. Because the competitive advantages of companies like them, they're just so. strong, but the valuations, they've been outrageous for years now, and you can't expect to get a good return if they're all valued. And so for me, I like to think about it like this. And this is a calculation I did some time ago, really to simplify and illustrate how much

Starting point is 00:50:43 it means to buy in at a good price. So say that you were to buy a stock, or call it 100 bucks, and you will be buying into that a fair value, and the intrinsic value of that would compound 10% a year. So after 10 years, you would be making something like 10%. I mean, it's a pretty standard calculation. Now, if you bought this as $200, so you bought it $200, even though that the fair value would be $100, but the intrinsic value would still compound 10% over the next 10 years. You will only be making a 2.6% return.

Starting point is 00:51:17 Compare that to 10%. And even worse, I mean, if you compare this to buying this $100 worth of stock for $50, and you would still again compound 10% a year for the intrinsic value, you could be expecting an annual return of 17.9%. And that's just like to give you an idea how much the valuation means. So whenever I look at the stocks out there again, it's not because the stock market per se or the companies are bad. I mean, they're still great, but the evaluations are just outrageous. And I have a really hard time entering a stock market or companies that expensive. So I, you know, it's obviously very similar to the way stick looks like it. I'm going to

Starting point is 00:51:54 describe how I think about it as a business owner, then I'll describe it as if I wasn't a business owner and I was just if I had a job and that was my only source of income. So as a business owner, I think of it in terms of really kind of two outcomes. As soon as we bank retained earnings or net income in the company and then it goes into retained earnings, there's two paths for me with respect to how that money can be employed. A, it can be employed operationally or B, it can be invested non-operational, meaning we're going to buy stocks, we're going to buy bonds, we're going to buy commodities, something that would be passively managed. Whenever I look at those two options, a lot of the times there's a lot more advantage for me to invest operationally in

Starting point is 00:52:40 my own business creating assets than non-operationalally because there's the second and third order effects that drive more revenue to the company by investing operationally, opposed to non-operational. So I'm immediately thinking in that direction first and foremost. And I think that when you look at people like Buffett, this is one of the biggest misnomer's that people don't understand is I believe he thinks the exact same way. I think he's always looking how can I invest operationally way before what stock can I buy. That's what he's doing with the stuff that he doesn't have ideas for, really is what it comes down to. So if I don't have any good ideas on assets that I can organically create for my business and I really have nowhere to go with that retained earnings.

Starting point is 00:53:23 Then it's going into, well, what's the stock market look like? What kind of yield can I get there? What's the commodities market look like? What does the fixed income market look like? If I feel like all three of those are overvalued and in a position that's precarious, then maybe I might even sit in a currency, which is kind of where I'm at today, to be honest with you. So that's how I think is a business owner. As an individual, I think very similarly about the situation. Let's say I work and I make $50,000 a year. That money, you know, is mostly going to just living expenses. Whatever's left over, I would think, how can I invest this operationally?

Starting point is 00:53:59 How can I invest this back in myself? Well, the most obvious answer for me is in learning, in books, in educating yourself because that's how you really accumulate a competitive advantage over everybody else is by just being very knowledgeable. So I would tell you, go buy a book on learning how to program. go pay for a subscription on a website, go to YouTube, get it for free. This is the thing that I find fascinating. You can go on the Amazon and you can buy books that are like top selling books.

Starting point is 00:54:27 In fact, the best selling books on Amazon are often the cheapest books to buy because you can buy them used for sometimes at 99 cents. And then you pay like $3 or $4 for shipping. So when you think about the investment in yourself at $5 total cost to have it delivered to your door, that's insane. to think that people are passing up on that operational investment all the time. And so I would tell you, so if you're investing operationally, call it books or whatever you're doing, call it $100 course or something, you know, if you're investing in that and it takes you a month to complete it, you should have quite a bit of money and retained earnings left over to invest non-operationally, or you can invest in another ideas or something that maybe you're coming up with a new business or I don't know what it is.

Starting point is 00:55:16 you come up with whatever your passion is. That's what you need to create something around. That's where you need to be investing your money first. And then whatever's left over is where you're putting it into the stock market or whatever the yield is. And then we could get into a whole discussion on that non-operational side. To be honest with you, that's what we mostly talk about on the show, despite, I would argue the operational stuff is probably more valuable.

Starting point is 00:55:38 Nice. Good answers, gentlemen. Thank you so much for calling into the show. It's so much fun for us to meet members of the audience and to, interact like this and to answer questions because, you know, it helps us with our thinking, too, because a lot of the times we don't necessarily state out loud how we actually feel about things. And, you know, Stig and I are business partners. So sometimes it's good for us to hear how each other think about certain things too.

Starting point is 00:56:02 So that's good. We're going to hook you up with a free subscription to any online course that we have on our website for calling in. And we really appreciate you taking time out to talk with us tonight, Brittany. Excellent. It was my pleasure. Thank you so much for everything that you guys do and keep it up. I'm loving it. Awesome questions, Brittany. Really? Okay, Preston. So let's call up the next listener. That's Jay. Hey, Jay. How's it going? Hey, Preston. I'm good. How are you? Doing good. Good to see you guys. Yeah, great to see you too. You're going to be on the show. You're all out in the West Coast, right? That's true. Yeah, I'm in Seattle. Oh, my. So, Jay, this is how.

Starting point is 00:56:49 how the segment works. You can ask Stig and I anything that you want and you can bring up any topic. There's a strong chance we might not know the topic, but we'll try our best. Okay. Okay. And we'll go from there. Okay. So I actually had a couple of questions that I was really curious to get your thoughts on. The first one was it's a bit of an experiment that I've been doing. So basically what it is is that if I have a stock that I'm interested in, let's say I've valued it with the margin of safety for some price X, but it's still a bit higher right now. It's sort of it's trading at 30 to 40% higher than X. If I wanted to protect my downside, I'm thinking how do I reduce my basis? One thing I've been looking at is to set a cash covered put option. You know,

Starting point is 00:57:37 that's with the strike price of X, that's, you know, far out. And if I get the premium, once I sell the put option, so that helps lower my basis. And if the price ever hits and the option gets exercise, then I don't mind owning the stock. So I was curious to know what your thought is and that sort of a strategy, what are the pitfalls? And what do you guys generally think of that approach? Yeah, you know, I generally like the approach. And I know that this is something that Toby also does from time to time. You know, it's typically also presented as a nice way to make money while you're waiting. One thing definitely to keep in mind is also that it's not free. We're definitely also paying a lot of commission and you also tied your money up, which is

Starting point is 00:58:19 another cost. One could argue that that is not a huge expense right now because of the opportunity cost being a little low in the market. Not the risk, though. That's a huge opportunity cost if you do enter the market. But if you're just looking at the individual stock and you're saying, well, you know, this is a good price. This is a price I would be comfortable with. And definitely. I would imagine, though, that the stocks that you're talking about whenever you're saying like 30% less, it's not a lot of money that you would get out of a position like that based on how options general a price. Could you give us on like some numbers in the terms of the returns that you can expect? So something that is so far out of the money? Yeah. So I typically have been looking at

Starting point is 00:58:57 something like one year out positions and then a strike price, which is 30% lower than what it's currently trading at. And so I've been typically getting two and a half, three percent yields on those. So it's sort of like a dividend almost. So it's not a lot of money, but you know, I'm just waiting and the cash is this there. I'm not doing anything with it. So I'm just sort of partially committing it to if it ever hits that price, then I buy it. So it's like around three percent ish is what I get. Okay. And what would it be net after commissions? Because I mean, that's another concern. I mean, obviously, how much would it cost for you to take a position like that? Oh, so I mean, it's just a standard commission. So if I do it through fidelity, whatever,

Starting point is 00:59:37 it's going to be whatever their rate is. So it's not a lot of commission. So, I mean, it depends on the broker, obviously, but it's a standard commission. So it's a 3% I told you this post commission. So Jay, let's say that there's a lot of momentum behind the market as it comes down through that 30% drop. Would your strategy at that point be just, I've got to buy it and then I'd quickly release it back onto the market for whatever the, you know, hopefully you'd be able to basically buy sell without any much of a delta between those two price points. Is that what you'd be thinking if there's a lot of momentum behind the sell off? I was thinking mostly just as a long position. So if it hits down and, you know, it comes off 30% and then it's at the point where I'm forced to exercise. the option and then buy the stock, then at that point, I just treated like owning any other

Starting point is 01:00:21 stock. You know, if I'm really happy with the stock, then if it goes down, then maybe I'll buy some more. But the thesis behind it is that my research should be strong enough that if it's a good long-term buy, then I don't mind entering it at that position. And then I'll just treat it like any other position. I think whenever I'm thinking through what you're describing, I think that it makes a lot of sense if you're a value investor, if you're wanting to get something at a certain price, you feel comfortable with the assets of the underlying business. You've done your research on the underlying business. I think it makes sense. So the other question is oftentimes when sort of I'm looking at balance sheets, you know, the assets part, I always have difficulty in

Starting point is 01:00:58 understanding, you know, how current those are, like how marked to market those are. So I've been trying to get a sense of, you know, how I can go about getting a good framework for knowing whether those are actually market or what industries are sort of current. and what are not, what lag behind. So I was wondering if you guys have some good resources or some advice on how to get a sense of how current or how much should we trust the assets on the balance sheets of the companies. Well, I think the best resource to really go into detail with that

Starting point is 01:01:31 would be security analysis by Benjamin Graham. And it's not the easiest book to read. I do want to say that. But he actually has a really lengthy, very interesting discussion about how to determine what the value is of the assets. And basically, when you look at a balance sheet, what you have at the very top, that is what is most liquid. So that would be your cash.

Starting point is 01:01:53 What you have down the very bottom, that would be was least liquid, which will be your property plant and equipment. You're basically looking at a variety of different assets. And, you know, if you take the top half, that would be your current assets. So that is what you expect will turn into cash within the next 12 months. But even so, even within that category, you cannot. equate everything to cash. So it really depends on how you look at it. For instance, how much inventory do you have? Inventory is not as liquid as cash is. And you cannot necessarily expect

Starting point is 01:02:23 to get the same return of inventory. So you basically need to kind of know which type of product of the selling. Can I expect that to be converted into cash? And when it comes to, for instance, property plant and equipment, you really can't take that as the carrying value. The carrying value basically means, you know, if it says $3 billion, that's the carrying value. You can't really use that. The way that, for instance, Seth Klaman is talking about that, Seth Klaman, the author of of a margin of safety, is that you should ask yourself, how much cash flow can I expect to generate from the property of plant equipment? Because the number that you see list is, is really an accounting number. I guess you can say that about most everything on the balance sheet, but especially

Starting point is 01:03:08 that line is so much an accounting number. Like, how do they decide to depreciate it? What's happened? Like, they can do all kinds of things with them. But the property plan and equipment, the way to value that is what is the worth? What is the cash flow they can take away from that? And then put that as your value instead. So if the asset goes back onto the market and it's going to be repurposed, and you're really

Starting point is 01:03:32 kind of looking at this factor more from a liquidation standpoint than anything else. but if it can be repurposed very easily, then the value might be worth more. And again, you're looking at the actual cash flow that it could be, that it could generate after being repurposed. I don't know if you've ever read the book, fooling some of the people all the time.

Starting point is 01:03:51 I've heard of it and it's been in my list, but I haven't read it. Yeah, so this was written by a billionaire. His name is David Einhorn. Okay. And the book is almost an entire book around this question. Okay. Okay. That's awesome. I don't know that it's necessarily going to help. you determine what the value of certain assets are on a balance sheet or the liabilities.

Starting point is 01:04:12 But what it's going to give you is an appreciation for how much companies can manipulate that and how little the SEC does about it. Okay. Okay. And the SBA. I think that's pretty cool. I think I'll check that out because in general, I just want to get a good feel for what kind of companies and what kind of industries have different levels of how they are manipulating it and what form and how much to trust it.

Starting point is 01:04:38 So that's really what I'm looking for. So that's pretty cool. I'll check that one out. Yeah, it's interesting. He talks about how he was a short seller against a specific company called Allied and how he was able to identify that they were using wrong accounting practices.

Starting point is 01:04:52 And a lot of it had to do with mark to market and stuff like that. And at the end of the day, he went and told the SBA because they were actually implementing SBA laws and actually should have been using SEC laws. And it was quite an amazing read, very meticulous. read, but you gain a deep appreciation for what companies actually have quite a bit of latitude with and how little they're able to do about a lot of it until maybe the company goes bankrupt and then it all falls apart.

Starting point is 01:05:19 But for Allied, that never happened. The company that he was, that he kept bringing this up over, it never went bankrupt. And so they just continued to get away with quite a bit of stuff that was quite interesting to read about. That's awesome. That's great. Thanks so much for the answers and explanation. I'll definitely check those out.

Starting point is 01:05:35 Regarding intelligent investor, is that still very relevant? Like, the methods and explanations that Benjamin Graham has given in terms of the kind of companies and industries we see. Because like the more and more we move in the tech industry and a lot of the stuff is quite different from his era, right? So how current do you think that still is? I think it's very current, perhaps especially because we're moving into technology. Again, I don't know what's going to happen. I definitely think that you will see a massive disrupt. in almost all industries. But it's kind of like whenever your internet came out. Eventually,

Starting point is 01:06:11 you all realized that the internet was used to stay and it's going to change everything. But it was not people who were calling it creating internet that was successful. That was not it. I mean, a lot of money profit from that, but it was really, really hard to see who would eventually come out on top of that. Yesterday, everyone would say that it's quite obvious that Google has such a competitive advantage, has the best product. But I don't know. remember when I was growing up, everyone was using Atta Vista. I mean, that just disappeared. So I think his teachings about how to value businesses and how to evaluate businesses, arguably from a very quantitative standpoint, I think that might be even more important today

Starting point is 01:06:51 because it's so easy to find a good narrative about, for instance, artificial intelligence. Previously in this episode, we talked about driverless cars. It would be easy to say that driverless cars would be a big thing. Yes, but that doesn't mean that if you invest in a company who created these cars, that there would be a good investment. We're talking about the technology and we're talking about stocks and it's two very, very different things in my opinion. So in the past three to four years, value investing has underperformed momentum. And I think that any time that that happens, and it almost always happens during the last phase of a business cycle, the last part of the credit expansion of a business cycle. And so typically what you'll have is you'll

Starting point is 01:07:31 have people that start bashing, the value investing, they'll bash the intelligent investor and all that stuff. And I think there's a good reason for that. If anyone's interested in understanding how these two things go hand in hand momentum investing and value investing, I'll tell you, look up Wesley Gray because he has some amazing research. I agree with Stig. I think value investing, especially the intelligent investor, all that stuff is alive and well. It's more of a function of where central banking has kind of taken things, you know, within two or three years, it's going to be a different story. Cool. No, that's awesome. Thanks for the resources. I'll definitely check out those ones. That's really good. I definitely have read an intelligent investor. I was sort of wondering, you know, how much has

Starting point is 01:08:14 changed since the time he wrote and how much of applicability do people still follow it. But it definitely sounds like it's still one of those books, which has stood the test of time. So probably worth the rereading it again, reinforce those ideas. The problem with the intelligent investors, it's just so dry. Yeah, it's kind of hard to read it. James. Yeah, Benjamin Graham's writing style, I mean, is just extraordinarily dry. I mean, it's very academic. He backs everything up and you'll especially see this in security analysis where he'll present an idea. He'll say, I think this is why things do this. And then he'll provide 10 case studies to back up his research. Now, what's interesting is when we read Joel Greenblatt's book, who now teaches Benjamin Graham stuff up at Columbia, Joel Greenblatt took a very, very similar approach to the way he writes his books. where he presents an idea, then he provides a bunch of analytical proof and statistical proof through actual case studies. But Joel Greenblatt's book was a blast to read. Yeah, I mean, his writing style was definitely not too easy. And the other thing is just that, you know, the book was written in

Starting point is 01:09:15 1949 and security analysis even before that. And it's just, it's a very hard language just to read simply from that. So I definitely feel your pain, Jay, especially if you want to reread. I'll definitely check out executive summaries. Maybe I'll start there and then that server as a guide for me to read it again. Well, Jay, for coming on the show, what we'll do is we'll give you the free subscription to our Intelligent Investor video course that we have on our website. Awesome. Just to say thanks for coming on and asking us questions and being part of the TIP community.

Starting point is 01:09:47 We can't thank you enough. Oh, absolutely. I think the pleasure is all on my side. I've been a big fan of your show. I've learned a heck of a lot and just look forward to listening to you guys every week. So thank you for having me. Great to have you. Jay. All right, guys, so that wraps up our 150th episode of doing the Investors podcast.

Starting point is 01:10:05 We want to say a special thanks to Brittany and also Jay for coming on the show. That's always so much fun to do those questions live and have that interaction. We hope you guys really enjoyed the episode and we look forward to doing the next 50 and doing another show like this at episode 200. All right, guys, that was all that Prest and I had for this week's episode of The Investors Podcast. We see each other again next week. Thanks for listening to TiViVee. To access the show notes, courses, or forums, go to TheInvesterspodcast.com. To get your questions played on the show, go to Asktheinvestors.com and win a free subscription to any of our courses on TIP Academy.

Starting point is 01:10:43 This show is for entertainment purposes only. Before making investment decisions, consult a professional. This show is copyrighted by the TIP network. Written permission must be granted before syndication or rebroadcasting. Thank you.

We Study Billionaires - The Investor’s Podcast Network - TIP150: Questions from the Audience, Artificial Intelligence (AI), Options Trading, Value Investing, & The Intelligent Investor (Business Podcast)

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