Yet Another Value Podcast - Perfecting the Investing Craft with Caro-Kann’s Artem Fokin

Episode Date: August 21, 2025

In this episode of Yet Another Value Podcast, host Andrew Walker welcomes back recurring guest Artem Fokin for a wide-ranging discussion on perfecting the craft of investing. Skipping individual stock pitches, they explore long-term process improvements, the role of AI in research, and how expert calls have changed their approach. Artem and Andrew debate the statistical validity of track records, the impact of conviction borrowing, and the future of market efficiency in an AI-driven world. Listeners will also hear how Artem's investment focus evolved over the past decade, and why understanding customer-level value is now at the core of his process.__________________________________________________________[00:00:00] Andrew introduces episode and Artem[00:03:07] Perfecting the investing craft[00:05:26] Bannister effect in investing[00:06:45] Inspiration from investor track records[00:10:40] Concentration and statistical significance[00:16:45] Bitcoin investor vs. great allocator[00:19:11] Betting on long-term outliers[00:22:43] Power laws and convexity[00:25:17] Will AI dumb down markets?[00:32:37] Market becoming more consensus[00:34:36] AI pricing medium-term alpha[00:39:06] Democratizing research tools[00:46:54] Early edge in illiquid stocks[00:48:55] Artem’s biggest process change[00:52:48] Can customer feedback mislead?[00:56:09] Golf clubs and process analogy[00:59:11] Avoiding groupthink and conviction leaks[01:03:39] Know who’s pitching the idea[01:06:25] IRR-driven sizing pitfalls[01:10:38] Penalizing leverage in rankingsLinks:Yet Another Value Blog: https://www.yetanothervalueblog.com See our legal disclaimer here: https://www.yetanothervalueblog.com/p/legal-and-disclaimer

You're about to listen to the yet another value podcast with your host, me, Andrew Walker. Today is a kind of special episode. I have my friend Artem Fokin back on the podcast. Artem is one of the most frequent and popular guests on the podcast. He's come on seven or eight times, and we do something different. We don't talk about an individual specific stock. This is just Artem and I coming on and kind of rambling for about an hour, over an hour, about process improvements, things we're thinking about ways we've tried to improve as investors over the the past 10 years. All sorts of stuff. You know, we start touching on AI and expert calls a little bit. This podcast is going to be sponsored by AlphaSense. We actually are doing a separate webinar

talking specifically about process improvements and AI and expert calls that I'll include a link in the show notes and talk about more throughout the podcast and stuff. But I think, you know, investing is, it's a mental sport. To Artem and I, I know it's a craft. We're always thinking about how to improve, all that sort of stuff. So I think you're going to really enjoy this conversation about just art and I talking about ways we can improve, ways we think about investing, all that sort of stuff. So it's a little bit different, but I think you're really going to enjoy it. I had a really, really fun time recording this.

So we're going to get there in one second, but first, a word from our sponsors. Today's podcast is sponsored by AlphaSense. Look, AlphaSens and Satigas are two of my longest time subscriptions. They're two of the podcast, longest time sponsorships. I love them both, and I'm so glad they merge. The product is awesome. I've done so much work with them.

I consider probably, not probably, definitely the most valuable subscription I've got between Alpha Sense burgeoning AI tools, they're getting better every month and particularly the expert library on both their, on both them. Look, I'll give you a little secret. I'm always pushing myself to be a better investor. And one of the ways I'm trying to do that is I've pushed myself to once a week, I do an expert call on a company in our sector that I'm researching come rain or shine. And it's just a really interesting way to be tapping into new ideas, people who are actually operating, get out of the spreadsheets, get out of the SEC filings, and actually talk to somebody about what's going on in industry. I do that myself, out of pocket. Alpha Sense doesn't pay,

Tegas doesn't pay. That's just me. But I mentioned that because, look, I think it's really continuing to help improve me as an investor. You'll notice it in the podcast when I talk to people. I do expert calls on the companies we're going to discuss. And I just show it, like, I get real value out of it. And if you're a fundamental investor interested in learning more, diving deeper, I think you will too. So Teague itself, I love the product. They've been a long-time sponsor, and I'm happy to keep having them on the podcast. All right, hello, and welcome to the Yet Another Value Podcast. I'm your host, Andrew Walker, with me today. I'm excited to have on one of my best friends in the industry, one of my favorite guests, Arden, how's it going? Hi, Andrew. Great in you. I was

actually thinking about wearing the same polo, yet another value podcast today for the recording, but decided that maybe it will be one too many. figure out you will be exclusively wearing this. Well, is it because I told you last night I was going to wear this? And then you said I might wear my famous pink polo to go with it. I think you often wear that for recordings. Sometimes. But we've got a lot to talk about today.

Let's just start. Before we get there, quick disclaimer, nothing on this podcast investing device. Full disclaimer, always at the end. And I'll just roll it into, look, add to disclaimer, Arden and I are kind of doing a different podcast here. We are taking, Arden's one of the most popular guests. I believe it's his seventh or eighth time on the podcast. We did a podcast earlier this year that got a lot of great feedback where Artim and I interviewed him as one of the keynotes at Planet MicroCap.

We're doing this podcast. And then I think we're going to do a follow-on webinar with Alpha Sense talking about using AI and expert calls and investing. So I'll include a link to that in the show notes. But this podcast, we're just taking a step back, no real individual securities and just talking about the investing process, things we learn about, things that Artie and I talk about while I walk my dog Penny, what, two or three nights a week every week. So that's the overall feel of this process. Artem, anything you want to add before I kind of jump into a first question and we just start basically rambling for an hour or knowing you three hours. Yeah, knowing me and you, it will be not an hour.

It will probably run longer. Yes, I think if I were to ask to summarize the theme, the topic, the subject of today's podcast, I would call it perfecting the craft. Yes. Investing is a craft. Yes. It's a profession. It's a craft.

And we as craftsmen would like to improve. And you reach that improvement through iteration, through feedback loops, through talking with other people in the field and getting their feedback on your own process, on your own process of conducting that craft, and sometimes cloning or stealing their ideas. I'm not talking about stock ideas. I'm talking about process ideas,

how to play this game at a better. I love it. It's like you're in my head. So I've got a post. It's in draft. I'm going to put it up at some point. But if I can ramble for a second, the post is all about, as you know, I ran a high rocks race recently, right? And when you run a high, when you run any type of race, it's kind of really clear who's better than you, right?

You run. And if they finish, if they cross the finish line at a time that's fast than you, they're better. And one of the interesting things in all sports, when you train hard and you run a race and then you see someone runs it faster, psychologically, you know that barrier can be broken and you can push yourself harder and beat it. The most famous example is Roger Bannister, right? He runs the four-minute mile and people think it's physically impossible. Two months later, someone else runs a four-minute mile. Now, good high schoolers will run four-minute miles, you know? And I always think about that like in investing, what is the thing that pushes you harder? Like, I'm sure all of us are

internally driven competitive people. What is the thing that pushes you harder? I will tell you what it's not. It is not, hey, investor A generated 300% returns last year. I generated 4% returns. They're so much better than me. I need to push harder because that has no context. They might have just YOLO call options. For me, what it often is for me, if I can try to land this plane, is when I talk to people like you and I say, hey, Artem, walk me through this thesis. And you say, oh, yeah, as part of this thesis, I, you know, I did five expert calls on Tegas. And one of them uncovered this nugget that no one knew. And it sent me on this rabbit hole. when I hear someone and they did great research and I'm like, oh, I wish I had that insight

into an idea I had or I wish I had that in the ideas I had. That's kind of what pushes me and that's like kind of the process improvement for me. And there's lots of other things. But I've kind of rambled. Hopefully I nailed that plane. You can just respond or tell me if you agree with that. That's interesting. For me, great investor track records do actually motivate and inspire me. But I'm not talking about a single year. I'm talking about an extended period of time with very strong returns over that period.

For example, if someone did 25% Kager for 20 years, they're probably one of the best in this profession. that ever lived, especially if they do something similar conceptually to the style investment type of prices. In other words, if it's Renaissance technologies with medallion fund, sure, they play a different game, it's not what I do, it's not what I know how to do, and we'll probably never know how to do. So that is not that inspiration for me. I'm intellectually curious about how they did it, but it's not something that will be inspiration for me. If you pick someone, let's speak a great investor, David Turner, who supposedly, based on publicly available information,

had a fantastic track record for many, many years. That is inspiration. Or Gerald Greenblatt, his track record, a part of that, I think, is published in the back cover, of the book You Can Be Stock Market Genius. That's inspiration. So, single years, probably not inspiration for me, probably will not push me, work harder or think differently, but, they will probably inspire me in the long term. Can I ask you a question? So I generally agree with you, but I've gone a little bit back and forth on this,

and I'll tell you why. I was talking to someone with a very good track record, I mean, borderline elite track record, who runs it also like, who runs very diversified. You know, they're running like 30 to 50 positions plus long shorts. They're running diversified, lower nets, all this. And I was talking to them about,

someone who's got a great track record, right? 14 years, super concentrated, very good track record. I was like, hey, and they said, look, I've talked to this person, I've interacted with them. I'm very skeptical that this person is a good investor. I was like, oh, well, track record, you know, like scoreboard, bro. Like, look, this person who's concentrated over the 14 years, they've had 10 positions, right? You don't have statistical significance. They literally could be the famous monkey that flipped a coin, and one of the coins came up very heads, right? Like, if 10 years ago you had plowed into Tesla, your returns would be incredible. I don't know if they're skilled there or not.

Now, you know, again, stock price, bro, like scoreboard, bro. But is that statistically significant? Like 10 years and in a concentrated fund, it's not a lot of investments. I think about, I think the person is clearly wrong. But the person who said, hey, Warren Buffett still doesn't have enough investment. to be statistically significant. I think that's wrong, like 50 years, but I do hear if somebody started in 2012

and invested today, if they had bought Mag 7, they basically would be a legendary investor. And I'm not sure, like, hey, they had the insight to buy Mac 7. Should we give them credit as a legendary investor or not? I don't know, but I've come to be a little bit of skeptical. And part of that might be because, you know, you see some, I read all these business books.

Me and Burr and Hobot on our book discussions, and it's like a little bit of a running joke. Everyone you read, the person had a near-death experience. And you're like, they created incredible returns, but any return stream times zero is zero. And like in the first 10 to 12 years of their return stream, there was this thing where, like, if things had broken just a little worse for them, it would have been a zero. And so, again, throw a lot out there. But I've gotten just a little more skeptical of even like longer-term track records based on that.

How do you think about that? I don't think this question is answerable. meaning we cannot, through deduction or induction, or inference, we cannot get to a conclusion that we would feel 95% confident that's a accurate conclusion. I don't think it's an answerable question. It's almost a question of belief. Do you believe in X, Y, Z, or you don't believe in X, Y, Z? Now, if you believe in Santa Claus, probably it's a little too much.

But there are other things that are issues of faith, of belief, that people do not need prove. So to answer this question, and remember, I don't know the fund you're talking about. I don't know the person who is skeptical. I don't know the person who's delivered those decisions. I have literally as much information as anybody who is listening to this podcast right now. So I don't know. My logic is what I'm thinking is this. Very often when people say John and this hypothetical John, so if any Jones in our network, we're not a lot of talking about you. John is a great investor. What it means more often than not is that the speaker and John are fairly similar in their thought process. And the speaker thinking of himself

that all of us are above average drivers and all of us above average runners and swimmers even if you don't know how to swim. So that was a joke Andrew. So, No, I was laughing because in college, I was like, I'm an above-average swimmer, and then I hopped in a pool. I was like, oh, I barely know how to swim. And what it means is that it's a similar to me bias at play. And when that person means Bob, again, hypothetical Bob, who does something different? And the person does not fully understand Bob's style or Bob's thought process. or decision-making, they think that they're not a good investor,

even if their track record may indicate otherwise. I'm not dismissing your argument that's maybe not statistically significant. I get it, but that is not answerable. I'm just sharing how I view it, and as I said, I cannot prove that my way of thinking about it is right, but I think that it works for me. For example, some people are superior business analyst. They're just fantastic.

They're at the top. Sometimes that translates into money-making, sometimes it doesn't. Some people are really good at idea generation. They may be not as good at the actual doing analysis, but because they're capable of finding great ideas, they make money. They will be people who are really good at risk management. And that's how they make money. They will be people who just have,

and that's the one that I understand the list of them. out, but I have some people in my network who I think have this incredible money smell. And I talk to them about the position. And I think that they don't know this position as well as I do, even though I don't own it. But that person still makes money. They do some analysis. And I'm not saying they are bad and saying that that's not where they make money. But there's something else about them, about their psych, about,

their process thinking, I don't know. But they make money. They have the money amount. And remember, I come out with the short money smell because I cannot come up with anything better. So in my, if I voice sign probability, my base case would be your friend who did not acknowledge the returns being deserved by another friend of yours. My guess is that they're just very different. Remember you and I, when we were in Dennis, at Plant Microcar, we spoke about the superpower. Different people have different superpowers. And I think it's very important to have in your network people with different superpowers. And also, just for people who don't listen to everything that Andrew and I talk about,

the way I define superpower doesn't mean that you're the best at certain skill in the world. It means that it's your relative strengths. meaning and there is probably better at lifting weights than running or as we found out right now,

swimming. So in exercise, weight lifting will be and the superpower. In investing, different people have different relative

strengths and that will be their relative superpower. So that's just definition. So I suspect that those two people

you spoke about, they have different superpowers. That's why, and they don't recognize that, or at least one of them doesn't recognize that.

As a result, he doesn't speak that highly of the investment talent and ecumen of the person with very enviable, very impressive track record. That's my best guess. No, look, I think you're, let me just propose more hypothetical. And I'm laughing so hard because you and I were like, hey, it's just Artem and Andrew Rambling. Let's kick off with, we've got this buzzy like ESPN would lead it with first take question that we're going to get to in a second, but we said we're going to kick off with that. And then we got into a conversation

on statistical significance of investor returns. Let me just ask one follow-up question. somebody who bought Bitcoin 10 years ago and held it till today, I don't think it's crazy to say they have better returns than every investor that we know, right? Bitcoin 10 years ago was 200, and as you and I are talking, it's $115,000, right? So in 10 years, they've got, what is that, a 500 plus X-backer, right? They bought in any size, they've got just absolute groundbreaking returns. Is somebody who bought Bitcoin 10 years ago and wrote it till today? Are they a great investor?

because their returns would say that they are literally the best investor of all time. I would say that there is a very high probability that such person is a great asset allocator, which is different than an investor. That's number one. Number two, if that person, and the answer I think will be very context-specific,

If that person in 2012 had $3 million net worth and they put $2.8 million into Bitcoin, I would say, boy, Russian roulette also statistically works, but it's a dumb idea to play it. However, if the person with the same 3 million net worth in our example, and by the way, let's imagine also that the person is it 30 years old, healthy, has a good professional career, et cetera, et cetera. If the same person put even 10%, 300,000 dollars in our example, into same Bitcoin in 2012,

I would say they're probably a great asset allocator. And I would also say that they're probably, they may be good. And I'm saying maybe because here we're speaking about significant statistical sample of one. in your example, they're probably good or even incredibly good at envisioning different future states of the world. And one of those potential future states did play out, and it played out in an incredibly favorable, favorable manner. No, look, I hear you. It's just, I've been

working with a coach, and one of the things they keep telling me is, Andrew, you want the, you want a, you want a definitive answer to all these questions, and there are no definitive answers. Like, that's one of the, I suppose it's the beauty of investing. It's the art of investing, but, you know, the logical side of me, I want to, I want to read a story and I want to know the ending. And like, with investing, there is no right answer, unless kind of your Renaissance technology and you run, you know, a thousand trades per day and then you can find statistically significant ones. Like I discovered the Bitcoin thing, they were kind of a VC and they were right and it's scoreboard throw, but it's up. Okay. Anything else there? Because I want to get to our first take question.

Okay, before you get into what you wanted to ask me and somehow 20 minutes later, you still haven't asked me or 15. So I would also say when you mentioned that your coach told you like, oh, Andrew, you want to know the definitive answers to questions that are not, that don't have such answer. I would make things even more complicated. Because very often, If you are investing in companies that commonly call compounds, so I will try to maybe narrow the definition a little bit, company with the attractive union economics, with the long growth runway, executing that runway and doing it in a very good way

because it has strong management that can get execution done. So again, I'm not saying that this is classic definition. I'm just oversimplifying here. You are statistically betting on our liars. Meaning most companies will not deliver, let's say, 10 years of 20% revenues growth, right? Or 20% of earnings growth. You pick the numbers. Statistically, it's not going to happen.

So you are betting on outliers, which means that your base rate, if you just take all universe of stocks. will be against you. Now, if you start cutting and recutting that universe, and I'm making this criteria up, okay, so I'm not saying that this is a good criteria. If you say, I want a found owner, appurator with at least 20% stake, and I want companies that pick margins,

pick revenue grows, pick RIC, they got backed by Sequoia or Anderson Horowitz or any other great VC that we think are great investors, whatever your universe is. In that case, you may be churning base rates in your favor, but even with those, I'm not sure they will be massively in your favor. So you definitionally from the beginning cannot get a definitive answer. And if you get somewhat definitive answer, somewhat definitive in quotes right now,

then your base rate against you. You're better non-outliers in this example, in this style. Look, that's a really fascinating way to frame it. I love the, Warren Buffett had the example once where he had like a University of Chicago expert who said, oh, Berkshire, you know, there are three standard deviation outlier, there are four standard deviation outlier, there are five standard deviation outlier, and eventually the economist just put his money in Berkshire and invested in Berkshire. And like what you're kind of describing is, hey, an average, an investor, when you're betting

on these great compounders, the average company, you know, what is the stat? like 80% of the market's returns have been, it's not the 80-20 rule in terms of 80% of the market returns are driven by 20% of the talk. It's like 80% of the markets returns are driven by like 20 stocks or something over the very long run. I'm a little bit of 10%. It was a famous academic paper published several years ago by professor. I forgot his name. I think he's from Texas, but I can be wrong. And it was like 4% of stocks. Yes, yes, it was 4%. percent of return, et cetera. There is this.

I think there was some survivorship bias there and stuff. But I love what you're saying. The compounders are like, you're betting on a company being one of those 4%. You're betting against base rates. It's a really interesting way to frame that. But another thing that comes here is that, by the way, to defend that professor is that any people of this nature will have some limitations. It's probably impossible to make it perfect.

and obviously many people will say, oh, you miss this, you miss that. Sure. We're talking about here 80-20 rule, meaning the intellectual 80-20 rule. Do you get 80% of the insights, even if 20% of situations are not covered? So, but what is it? And since we got into this topic completely unexpectedly, so if you think about, if you say, I am betting on statistically less likely events from the base trade perspective.

So probabilities, if you do a random dart throwing, are against you. So number one, you need to figure out how to improve the probabilities. Sure, that's obvious. But number two, you need to have, I think when investors call it convexity, I don't know whether mathematically it's convexity or not. That's probably somewhat debatable, but let's call it convexity here. You need that convexity of returns on the upside. And this is where I think when investors can learn a lot from venture capital.

with their mindset and how they think about it. And obviously, the most famous one is the power law here. I think the first time I've all about it from fantastic book 0 to 1 by Peter Thiel. And then there is another fantastic book that I read two years ago, I believe. The Power Law by Sebastian Malaby. This is the same author who wrote More Money Than God. I think I should like the power law even better than like more money than God, but that's subjective assessment.

So that's a fantastic book about many great venture capitalists. Now, I'm not saying that public market investors should become venture capitalists. It's a different game. And by the way, by the time that these days, companies become public, they are way past those early stages where venture can invest. But the overall concept, the meta idea, the meta thought here, is that if you're investing where base rates may be against you, number one, term probability is somewhat more in your favor than the base rates, that's number

one. So do some filtering, cutting, whatever. And second, you need that explosive upside, where if you hold that company for 10 years, for example, it has a shot at becoming five bag or 10 bagger or pick whatever bagger will satisfy. However many bags you want, you could have. So yes. So I think that's another consequence of that framing. All right, we're going to hard pivot. All right, let's ask the ESPN first state question. You and I were talking the other day. We're talking about AI.

Again, we're going to do a webinar with Alpha Sense talking about using AI tools, using expert calls, which I think are, I don't want to spoil the webinar. I mean, I think over the past. Yeah, look, I just think over that 15 months. You can spoil the webinar a little bit. Don't do too much. Look, I've talked to some investors who are a little bit older than me, a little bit older than you. and they're like, oh, I haven't used AI at all yet. And I'm like, okay, cool.

Like, you're basically an investor in 2005 who's like, oh, yeah, I'm not using spreadsheets and email. Like, it's so revolutionary to me and it changes so much. And the expert calls, you know, I just think it's the only way to read, not the only way, but it's such a useful process for so little up front to, like, talk to people into industry, get real insights and everything. So they really evolve. Anyway, we were talking about AI because I was saying some of this. And you told me, hey, I, I,

I think AI over time is going to make markets. I don't want to put words in your mouth, but I believe you said make markets stupider. And in my interpretation, that meant less efficient slash easier to generate alpha. And I kind of disagreed, but I don't want to put words in your mouth. So I'd love to just ask you, am I remember this correctly? Did you think AI will make markets less efficient and kind of easier for active investors to outperform over the long term? First of all, I don't believe I said stupider.

I think you said stupider. I think stupider wasn't direct. I think I said dumb. Dumber, okay, okay. First of all, I think I would have said not stupid, not stupider, if I want to use that word. And I think I said dumbed. Okay, dumber. Let's go with dumb.

I think that's what I said. So let me, and that's, I'm not sure whether it's consensus view right now among investors. I would firm disagree, which is, yeah, I would firm disagree, and I would think, your view is quite non-consensus, though I don't think it's completely out of consensus. Okay, so let me talk about how I got to that thought. And also I want to cover out something. I'm not a coder. I'm not software developer. My typical solution to any tech problem is to restart my computer. And if that doesn't work, plug it out of power for 30 seconds and then plug it back in.

Yeah, I'm pretty, very, very clear about that. And I also want to say that, as by the way, it applies to anything that I say, but here it's a little more so. I could revise my view six months later or three months later or 12 months later or even tomorrow. And I am not a futurist. I am not predicting the technological future. But this is my thought process. Where we started with you today is that investing is a craft. And it takes certain time to master that craft.

The best way to master the craft, if you have a great mentor, you're working directly in front of him, for him or her, and you are learning and copying that. That's probably the best. If you didn't have that opportunity, maybe you figure out it yourself. You probably spend more time figuring out on your own, and you probably got more scars on your back from your mistakes, et cetera, et cetera, that sometimes was self-inflicted. but that's how you arrive to your process of insight generation and get into conclusions and in order to make strong returns you need to arrive into some insights into the business into the investment setup into something else that I usually fail and non-obvious And I think there is a danger.

I'm not saying it will happen. I'm saying it might happen. Going back to our conversation about Bitcoin, it's one of those future states of the world. It doesn't mean that it will be this state, where people, especially younger generation of investors, they will re-outsource their thinking to AI. I would suspect that your...

parents and my parents' generation, on average, and on average is the keyword here, are better at navigating a city without a GPS. I expect that our generation are worse. I also suspect that those who are now 25 are probably even worse than us at that. It's a skill of atrophy. And if I mispronounce, I mispron that you will need to correct me. So that muscle just goes away for people because it's so easy to block your GPS. And I think there is a real danger that may happen

with how people think, how people do research and how people develop conviction and investing. And AI is an incredible powerful tool. There is no doubt about that. Everybody says that. That's not insightful. But

what it will do to our brains is an open question. And I think those people who can figure out how as the AI stands today, and remember it can evolve, it can get a lot better, it can do something else that we can open think about right now. But as the AI stands today in early August 2025, I think people who are more likely to succeed are the people who figure out how to use AI as a supplement and who do not fall intentionally or unintentionally into the trap of outsourcing their thought process to wear. Also, and also something else.

You said more, you interpreted my point as more efficient. I think market efficiency or efficient markets, it's such a loaded term that everybody means something different that I would rather not use it. Okay. What I think might happen is that market will become more consensus. Because people will be asking roughly the same questions to roughly the same large language models. Now, there will be some variation in their prompting skills.

And variation in particular model that they're using. Some of them is better at any single moment in time because there is a race between them. Sure, there will be some variability. But I think the answer that will be given, would be fairly similar, big picture. And what it means, I think market will become more consensus. And if you figure out where AI is missing either important data points or information

is just not out there, meaning it has not been written on the internet, in articles, in export calls, somewhere else, your opportunity to generate outsized returns, because everybody else is running left and you realize that you need to be running right, I think will likely improve. That's what I meant by dumb and dumb. Maybe a true strong word, but it will be more consensus driven. So, you know, honestly, as you described it, I kind of don't know if I've got huge discreements here with you. And I kind of analogize like this. Like, I like to say, hey, if somebody comes to me right now with a P multiple based investment, right? They say, hey, Andrew, stock XYZ is trading at eight times price of earnings. I think it's a buy. I'd be like, cool.

there's no alpha there. You've just bought beta and probably negative beta, right? Because 50 years ago, 60 years ago, there was alpha there. There were not computers that were doing that. You could go kind of calculate that and buy something. About 20 years ago, 25 years ago, like computers got so good, they were automatically doing that and was kind of priced in. But what that meant was in, let's call it 2010, that got priced in and the returns to finding things that were non-obvious values. And I would think about many of the compounders you talked about, right figure out early google facebook all these sort of stuff amazon yes they did not trade for 10 times earnings so they weren't your traditional value but they had this huge growth one right

huge modes lots of ability to compound you know for many of them i don't think amazon was guaranteed but like facebook and google once they kind of locked in especially google it was kind of guaranteed that they were going to grow and continue taking share if you could figure that out and you could invest beyond oh google trades at 25 times next year's earnings and you could kind of see see the growth runway in the unit economics, you could make a fortune. And I think what you're saying is, hey, AI is going to price out maybe a lot of the medium term insights, right? Like, AI is going to have a lot of insights. Maybe it's going to price out the alpha in buying the compounders and buying the union economics. But if you can figure out like something that is beyond that, the returns

to that, you know, buying Google was exponentially better if you figured out that insight than buying U.S. deal at four times price earnings in the 80s was. If you can figure out kind of the next insight, that AI is not going to generate something that we can talk about what it would have to be. The returns that actually might be exponentially better. Am I saying that in summarizing that correctly? I wouldn't say that you're summarizing. I would say that you are putting another angle on this issue. And some elements, I think, are fair, some elements, I don't know.

I don't have a strong answer. Whether it will be, whether AI will be pricing out medium, term alpha versus long term. alpha I don't know. I don't have an answer. I don't think I'm prepared to figure out an answer right now. As time passes by, we may figure out. If you were to ask me now, I would probably say that. I think it will be more dispersed. I think it will be more consensus-driven. That's kind of how I think about it. Because if AI will be giving same answers, roughly again the same, to a bunch of analysts. And by the way,

getting answer from AI, in my opinion, is very different than reading 15 earnings calls or conference transcripts or listening to them live if you prefer listening. And reading or better yet conducting yourself tons of expert calls and getting slowly to that mosaic and putting it together,

I think it's very, very, very, different from the way you in terms of process, how you get to that conviction and knowledge, deep knowledge, ingrained in you versus reading a 30 or 40 or 50 page output from AI, which can be useful in certain cases, but it should not replace your brains and thinking. And then feeling that I have this knowledge, I call it fake knowledge, like you don't. Maybe you memorized it, maybe you didn't, depending on your quality of. of your memory and its capacity, but it's not the same as figuring out those things yourself.

So that's how I think about that. What I think AI will probably do is that different competitive advantages or competitive strengths would be a better turn that people have, may become obsolete, and then you need to reinvent yourself and figure out your new competitive strengths. I'll give you an example.

If someone was really good at writing 10 years ago, You're just like brilliant, right? You're engaging, you're convincing, you can figure out how to deliver insights and punchlines. That's a fantastic skill. Not everybody can do it. Guess what? Now, you still may be better than everybody else. But everybody else with the AI, the gap between you and everybody else who is average with the AI has closed dramatically.

And I think it's overall theme, by the way, of technology and technology. based investment tools, research tools, including AI, including expert library, including expert calls, is overall democratization of access to information and knowledge. And also, I believe, closing the gap between investment firms or investors in general with different financial resources. In other words, hypothetically, if some, let's pick expert library tools, for example, if 10 years ago, before export libraries came out, expert call libraries, and this is all hypothetical measurements, a person at $2 billion fund has 100 units of utility because they could access someone like bespoke expert calls and pay $1,000 to a price.

That's very expensive. Now, if you're a $2 billion fund, you have the budget, you're fine. If you have $5 million fund, for example, I'm intentionally taking something very, very small. You couldn't afford it. You're out. Then with Tigus advent and then stream and now it's all under AlfaSense umbrella, you can be subscribed, you can have access to a bunch of library, to a library. You can do your own bespoke calls at a lot cheaper price.

Yes. That democratization of access to information, knowledge, and insights. That's fantastic. So I think now, if before the gap between a small fund and a very big fund was 100 units or 99 units, gap still exists, don't get me wrong, but now it's maybe 20 or 30. It's a lot smaller. And I think AI is likely to do the same.

No, I think that's interesting because what you're seeing is like one area, again, 30 years ago, probably more than 30, but 40, 50 years ago, your edge could be, I can calculate price to earnings, right? Like, your edge was basically, I can do math better. And that gets, that gets arbitraged away by a lot of things. Quant fund first Excel, so everybody kind of gets it, and then quant funds start running and automatically applying it. I think what you're saying is, hey, the past 10 to 15 years, one of the areas of edge might have been, I can ingest, I can ingest information, either more or better, right? As you said, with the big funds, and you're 100% right, a big fund now, but especially 10 years ago, could afford to go do 10 expert calls and

throw down $10,000 of dollars of research expense on a position, whereas a small fund just couldn't do that. With expert libraries, that's moving away from AI, but, you know, a big fund might have had five analysts who could summarize every sell side, every cell side note, every expert call, all that, that AI can do that for a small fund now. So that summary of information goes away. The new tools is like the question is going forward, what is the thing that will kind of get amplified by AI? And I think I've talked and read about this.

And I mean, in my view, you can tell me if you disagree or if I'm completely misinterpreted the premise, like in my view, as that kind of ability to summarize big things of information gets consumed by AI and maybe simple thesises get spit out by AI, I think the next wave is kind of, hey, can you go find me things that are not on the internet or that are not publicly available? And obviously, I'm not talking about MNPI, but I'm talking about, hey, the next wave might be, you're researching a retail company.

Like, can you find non-obvious sources of data or, you know, you're researching a lumber company? Are you the person who can go and, like, go to a lumber conference and network with 15 people at the lumber conference? And so you've got to read on how everything's going that, you know, literally no one else has because it's bespoke. You created it yourself. I don't know. Do you agree, disagree there? I think we are looking in the same direction.

I agree. I will add a couple of examples or wrinkles or nuances. By the way, wrinkle is an interesting word. We can speak about it in a second. So I think I unintentionally created a pun here, even though I didn't think about it before I said it out. I'd say it again. My wife is so much better with puns and wordplay than me. that. What's the pun? No, I remember I said there may be a couple of other wrinkles on what you said,

and then I realized that my example is actually fits nicely into wrinkles. Okay. Okay. So this is big picture. I agree with what you said. I think situations where information does not exist in written form at all. That's one of the areas where it will be fascinating. And I think what will have, and also let's step back. And as you know, mostly Caracan Capital, the fund that Iran, invest in small and microcap securities. Not exclusively, but that's what mostly I do.

This is where I spend most of my time. So what it means? It means that general, if you go into, let's say, alpha sense expert collaboration or choose, and again, I will use them interchangeably because they're in the process of converging the two. If you go there and you put, pick $20 billion market cap company, probably there will be, I don't know, a few dozens of calls, right? That's probably pretty reasonable, especially if you pick, let's say, TMT, that I'm, more popular among investors to do export calls, et cetera. There will be a lot.

My ideal setup, I go to the export call library, I put the ticker in, and it gives me, there are no expert calls available, would you like to request one? Or maybe it's one or two. And ideally also, it's kind of old. In that case, if someone goes and says, and let's pick a real company,

Disclosure, the company that I'm going to mention, Carcan Capital, LLC, and or its affiliates, own shares of softwave listed in Israel. So, and by the way, that's the company that sells medical devices, for skin tightening, has a recurring revenue business model, razor, raise a blade, and one of the consequences, if you are a patient using that, you remove wrinkles. So that's why I will...

There's the pun. Okay, now I... Yeah. which I said absolutely accidental, and then I like, oh, I should use this an example. When I learned about companies' existence first and started doing research, there was only one expert call on alpha cells. That's the call library that I use. Only one, not many. And by the way, it was somewhat negative, in my opinion. Or at least it was pointing out areas where the company needs to improve itself. And by the way, in my opinion, by the time, by the time, by the time, by the

time when I read the call, I thought they've already fixed all those things that the former employee was mentioning. So I felt like, okay, I guess probably they were reasonable concerns, but as the company has grown, they fixed it, fine. But that's all. So, okay. And remember, this company listed in Israel, financials are in Hebrew. I don't speak Hebrew, unfortunately. How are you going to be interacting with AI in that situation? One expert call, filing. And sure, different titles will be happily translating those filings for you. In fact, I use them for that. But it's very different to interact with the AI tool in that case.

There is no information. Large language model needs to have language to give you answers. Something in writing. As part of my work, I've done multiple expert calls. I spoke with patients. Sorry, patients I spoke anecdotally. That's not expert calls. But I spoke with doctors who are the people buying the product

and then they will be engaging with patients, explain them the benefits of the treatment and using those devices. And I spoke also with former employees. She will know more about the company culture and market sales process. Everything will he will be asking. If you now go into library and put software as a ticket, here probably will get what? By now, eight, nine expert calls will be my guess, roughly. At this point in time, you can use AI to ask thoughtful questions.

Now, you also need to have thoughtful questions. So you need to have an investment process. AI will not replace your investment process. If you have a good investment process, AI will make it more robust. If your investment process is not robust or you don't have a good process at all, then AI will make things even worse. That's my subjective opinion. So now you can go interact with AI, ask questions, and you will get to those takeaways, conclusions, insights, punchlines a lot faster. What I think it means, original ideas with very little information in writing, and information will mostly qualitative, of course, will be more important to find early.

But then what I think will happen, the discovery period of those ideas and market reprising those ideas will probably shorten. Well, look, I think what you described fits into my thing, right? Like, I was talking about finding non-standard places of information. And in my head, I was kind of thinking like a $2 billion company where you go and you get cut checks at an industry conference or something that obviously on it, what you described was finding a company that was small enough that there wasn't much information out there. and you went and by doing the, in this case, expert calls and customer talks and everything,

you created the information for yourself, right? Now, that doesn't mean it's a good and bad investment, but if you have 100 companies with not a lot out there, you go create that all. Eventually, you know, if you're using a public expert call transcript, in this case, it goes public eventually, but you create all the data and then you feed it in. You do have edge because you created it. Now, hopefully you can, like, create that data and, like, kind of refine it and execute on it properly, right? Like that I certainly know there have been some undiscovered microcaps that I've

invested in that, you know, I thought I had edge on and I kind of wish they had gone undiscovered by me in the long run. But you are kind of describing creating, you are describing the same thing, creating information and having a data, having an information edge there. Let's talk more about expert calls in our follow-up interview tomorrow, which again, including the show in it. I want to ask for some, since this is supposed to be a process theme process. I think about process all the time. Let's ask you, what is something 10 years that you've done over the past like 10 years? You know, you are, I'm late 30s, you're early 40s. Theoretically, we both should actually be hitting kind of the peaks of our investment,

investing careers like right around now. What is something that you did 10 years ago as you were kind of like prepping for the prime of your career is the generous way I put it, that you've changed, that you've improved on anything that you think has made a note. noticeable difference in your investing process. Massively bigger focus on the customer. So massively bigger focus, more focus on the company's customers is well. Okay. And let's just give me an example. Early on, when I was Ryan Caracan, I made a very painful investment mistake

in a company investing, investing in the company called AgriFresh. I know Agrofresh. A significant loser. I think you and I spoke about that. So back in 2015, and with the full benefit of insight, if I spoke with a few customers, I think I would have probably realized that customer, while customers value the product,

there is, they are not as a, there is a difference between customers, satisfied customers and raven fans. Ideally, you want to be investing in companies who serve a constituency, and that constituency are raven fans. And I think I could have done a substantially better job and avoided a painful mistake,

both psychological and P&L, if I applied that framework to understanding Agrafresh. I could have done a lot better. Now, what it led me to is I need to understand customer value proposition better. And guess what? What's the best way to understand customer value proposition? Talk to customers. Ideally, if you could, visit them, right? Spend time with them. But sometimes it's an intangible product so you can speak with them and learn about their why they consume it, et cetera, et cetera.

And this is, by the way, I think doing expert calls is very, very valuable. Understanding that customer build a proposition and why customers are not switching or switching with the competing solutions, what they're buying process, et cetera, et cetera, who is the decision maker within the customer organization if it's B2B product, then you understand all those things. And the old school way would be ask your network. Now, that's the best, because you will get unfilled answers. But I have a reasonable large network of people from business school, from other venues of my life, et cetera, et cetera.

There are natural limits there. So you need to ask for introductions or introductions, such, et cetera. If you could find someone like that, that's the best. But if you couldn't, then either go LinkedIn and start calling, fantastic, or use expert call providers. In the past, it was very expensive. The cost has gone down as you and I discussed five minutes ago. Let me ask a couple questions there.

So I like that answer, as you. But I, you know, as I said earlier, I want one. rule, right? I want one rule is rule them all for everything I do. And one of the tough things I found with customer calls is two. Number one is, you know, what's this? I think it was Steve Jobs. He said, like, the customer doesn't know what they want until we give it to them, right? And sometimes I can think of recent examples. I don't quite want to disclose the company, but I'm sure people can think of something similar. A company is launching a product, right? And it's an improvement on the current product, but, you know, word gets out and people know. And I talk to, you know,

the customers, and they say, oh, you know, whatever, we don't really need that product. We don't think it's that much better. We're not going to upgrade, whatever. And then the product comes out. These are generally B2B products, but exceptions. The product comes out, and then you talk to the same customers two weeks later, and they're like, we love it. We're upgrading all of our equipment to it, like that type of stuff.

So that is one thing I worry about where the customer, like, they can be so fickle. And obviously that is they were talking about something that's yet to be launched versus launch, but they can be so fickle with that. I kind of worry that I talk to them and I have one opinion and then the next week, if I talk to them, I'd have a completely different opinion. Do you want to pause the recording, rewind to the place where we spoke with you about base rates

and how you're trying to recat the universe and how you're figuring out that you're betting on outliers? So this is my response to you. Like, I want one rule answer. I know, I know. Andrew, this is my one-rule answer to you. There are no one-rule answers at all. That will be my response.

Look, at the end of the day, what we as managers are getting paid for is judgments and decision-making. Everything else, talking to management, attending a investor conference, attending industry events, talking to former employees, employees, searching the web, talking to AI, reading, what are modeling, whatever you do. That's simply an input. It simply fits to the output. And the output is your decision, your judgment. And decisions are based on judgments. So I think what you're saying is this.

How can I make the same judgment all the time in similar set of circumstances and be right? And that says, I don't think you can. And it boils down to, and also remember it, it's a lot about new answers, so wrinkles. In your example, you started with Chief Jobs' famous quote. First of all, it's about B2C. Second of all, it's about the true breakthrough disruptive innovation, where the product does not exist. I don't think that question is not, it's unknown and unknown. wouldn't know whether that product will take off with customers or not.

And by the way, if you hadn't inside that this product will be fantastic, even when iPod came out, you would have been probably calling me right now from your BBJ from your own private island. No, you know, let me give you the other side of it. I know. Yeah. So that's B2C and that's unknown and unknowable. And then there is another end of the spectrum where a product.

are already there, it's a B2B software company. Let's pick HubSpot. I don't own HubSpot. I admire the business in what they've done and what they've built, etc. If a number of years ago, you spoke with 20 customers using HubSpot, you'll probably figure out that it's a very, very good CRM, market technology, SaaS company. And you would have probably figure out that the product is good and customers are satisfied and they're happy and maybe they're even more than satisfied. So that's a very different thing than talking to them about if you get this product that doesn't exist, but it has this feature, it's very different. So I think you need to calibrate the tools that you're using. Do you play golf? I did when I was a kid, but right now,

no. Okay, fantastic. But I know the rule is just fine. Yeah. Okay, so I don't play golf either. So that's why I will give you an analogy from golf, because I have no idea how to play it. So in golf, as far as I understand, there are very many different types of cups, right? I cannot believe you're giving an analogy from a sport you don't play. Okay. There is, yes. And depending on the terrain, depending on the circumstances, wind, whatever, you are using a different car for each situation. I think with your approach, you have one size answer to all situations. you're trying to go to play golf tournament

and you brought only one cup. You are correct. You are correct. You have all of them and need to figure out which one to use when. I can't believe how good of an analogy you just use with a sport that you don't play or apparently know the rules too

because that was excellent. So now, okay, I'm glad to hear that because otherwise will be very embarrassing if I give you an analogy that was totally misplaced. Let me go to Let me go to a completely different question. Okay.

One thing, you and I talk quite a bit. I think people can probably hear that. You and I probably talk quite a bit to, you know, the small cap value investor fund community is not large. We probably talk quite a bit to the same people. There are a couple stocks that, you know, if you go read, if there are 20 small cap value investors, you go read 20 of the letters, you know, 12 of the 20 managers are long the same one stock, right?

And I don't need to name names, but I'm sure. everyone, as soon as I said this, who follows the markets closely, can think of something. And guess what? It doesn't just supply it to small-cap managers, right? Like Bill Ackman for a long time, he'd go along the stock and then, oh, all of a sudden, there's 15 other people who run like medium to large-sized edge funds who are longed the same stock. Like, it happens. Tiger Club, the Tiger Club on lies. Oh, look, one of them is long, all of them are long. Like, it happens. You talk to smart people, you share diligence, you all come. You know, that's great. Hopefully you're talking to really smart investors who have

really thoughtful thesis is and stuff. But one thing I always worry about is it is very easy to start outsourcing conviction, outsourcing thought. It's very easy to get into groupthink. It's very easy to get into the same names. And even if you gave me one thing I worry about, and I know you mentioned softwave, and I have looked at this chart recently, and I'm kicking myself. You and I talked about quite a bit, and there was med device issues, and I can talk about those

later. But, you know, one thing I was about, I feel like I have a higher bar for. when a friend brings me a name because I'm worried that because I respect them, I will have a lower bar for it and group think. And then I'll just call them up after I record and say, hey, what you think? And then kind of rely on that. So I just want to ask you, you know, I'm running yet another value podcast. 330 episodes, 315 of them are individual stock pitches, right? When a listener listens to this and they love an idea and obviously not to invest in my mind, but they love an idea. When you and I talk, when you talk to other friends,

how do you kind of like dial back and avoid the group think avoid the hey I'm outsource thinking hey this person has biased me in an investment even if you're kind of going to like run the bases and do all the work on it how do you avoid somebody you respect telling you they they've put their money behind it they've put their belief behind it

and that's not going to influence you so first of all when you hit your 400 podcasts we've got to do another interview where I come and interview you I promise to wear yet another value you follow that you kindly gave me. There we go. Another interview, it will be the second. Remember, we did after 210.

We did. We did. And on this one, you're going to show up the yet another value podcast tattoo that you got as well, right? Oh, yeah. Yeah. No, I would not. But I can show a bottle. I can show a bottle.

It's very, by the way, very nice bottle, great size. So, um, the one line answer will be. In the situation that you describe, I want to make the conviction my own. Don't outsource conviction, that's a dumb idea, but make idea your own. There are some ideas in my portfolio that I probably got from a peer, a friend, whom I like and respect, the thought process, but by now I may know some of those ideas, not all of them, but I will know some of those ideas better than they do. and even if they still know it better

because they've spent longer time research in the company, et cetera, et cetera. I have my own conviction in those. So that's, I think, what we need to be aspiring to. So that's number one. Number two, you remember you said low bar, right? I don't think it's the right way to frame it. Low bar to invest in an idea because it came from a friend,

especially if you like a friend. So, I think, the lower bar should be applied if idea comes from a friend or respected peer to put that idea on top of your research pipeline. There are some friends in my network whom if they call me today and say, Artem, I have a great idea. Let me tell you about it. I'm so excited. This is an idea. I'll probably drop almost everything and we'll start researching that idea. I will not go and buy it,

but I will go and start researching. It will be, it will trump over a bunch of other ideas that have in my pipeline that I sourced myself or already I got from someone else's assertion. So I have a lower bar there, but my bar true best should be the same. That's what I aspire to. I'm a human being.

I'm fallible. There are probably cases where I did not execute to that vision that I just described and maybe I indeed lowered the investment box. It's possible. But that's my inspiration. That's what I force myself to do. And the way you force yourself to do it is via following your own investment process. If your investment process is, for example, to read three years of all earnings calls

and conference presentations. Do not shortcut just because idea came from a friend whom you like and respect. Go and do it. And again, I'm using kind of very simplistic, very cruel tools. If your rule of the investment process, read five customer expert codes or do your own if they're not available. And by the way, five is a pretty low threshold, by the way, especially in that

20 in the database. It's a pretty low threshold or 30 or 50. So you go and do the same number of calls that you always do. Either your own bespoke, if that's your process or if you're okay with reading someone else calls, fine, go do it. You don't cut it. If your job, if your process talk to management and ask them your questions that on top of your mind, et cetera, et cetera, do not cut that step out just because the idea came from a person who you deeply respect. So, and again, it's wanting to say, I want to have the same level of convictions I do on my own ideas. Sure, but the way you get it done is via your processes and your routines.

That's how you get it done. And then, also, this is very important about ideas coming from your network. I have friends. I'll use myself as an example. If I call you Andrew and say, Andrew, I get a great idea for you. First of all, I hope you will pick up the phone. You know, I'm saying, I'll call you when I'm walking penny. I'm with Sylvie.

I'll call you and say, Andrew, I found this company. It has 200 million US dollar market cap. It's listed pick a country where most people don't look at. And there is no cell site coverage. And there are no well-investance club write-ups. Or maybe there is one, but very little information. And there are no expert calls and blah, blah, blah, blah, blah. I would suggest that you listen to me carefully and thoughtfully

and maybe put that idea on top of your research pile. If I call you and say, Andrew, I found this great car, I found this great investment setup. It's a coal company on the East Coast, trading four times earnings, but they're going to pay a big special dividend. I think you should hand up on me. Why? I don't think I know how to do those investment situations.

That's not my cost strikes. And most likely what I'm calling you about is a pretty lousy idea. And I don't think you should have special dividend. I was like, I'm here for it. Artum, what's the company? You know, but remember, I usually don't call you with this type of ideas. So you need to calibrate when this, when an investor friend mentions an idea,

you need to collaborate whether that's a, place. Imagine the hypothetical investor with a very strong track record. But you need to figure out, in my opinion, I want to figure out where those returns came from, what type of patterns in situations. If those ideas came from call names trading three or four times earnings, returning capital, doing buyback or doing a tender or whatever, I won't probably listen to that person when he pitches those ideas. If that person made money on software as a service names, so software as a service company that are one to two billion market cap

and they're about to become $5 or $10 billion company in the next few years. I want to listen to those ideas from another person. So I think that collaboration is very, very, very important. No, that's super interesting, especially because there's a company you and I've been talking about that fits one of the descriptions

and it just rips higher in my face every day and I'm like, hey, at neither here. One of the great things about being podcast is I can ask completely simple questions under the guise of asking for my listeners. So I want to end with one question that has really been on my mind recently in terms of process improvement. We've talked a lot about ideas and stuff. But in terms of sizing, force ranking, however you do it, your ideas, how is that

change for you over time? That's a process. And that's a difficult one. And it's difficult one because you would never get everything perfectly right. And that's difficult. So, and this is just ecologically challenging. And we know this. I don't know other people who listen to this will know or will know this not about me.

In my prior professional life, I was an international tax law in New York working for a big American law firm. So think about this. I get an associate. I'm getting an assignment from a partner in a certain situation. Let's say, where do, our client wants to do a tax-free spin-off. Yes. Do we have the facts that will support tax-free treatment, et cetera, et cetera,

we need to do analysis as an example, right? So if I come back to a partner and say, hey, this is my memorandum, legal memorandum, I wrote, let's say, 10-page memo, and that memo is about 60% accurate. I think the tax partner should take me and kick me out from the 38th floor of 200 Park Avenue. That was our office was. And they would be right for doing so. Now, they're very nice people. I hope they wouldn't, but you see my punchline.

If you're right 60% of the time in your force rank in your positions or managing your portfolio on your expected IRR, you're probably doing pretty well. And that's a very challenging mindset for anybody. That's why I think people, for example, who play a lot of probabilistic games and come to investing, they may have a natural, psychological edge, because they used to that playing poker. They used to that.

They may have a good hand, and they may still lose it. So that's very different mindset. And for me personally, it has been a challenge to adjust since the time I left a legal profession, went to start a business school, then worked for another hedge fund, then started a car account capital. That's not easy psychologically. So what I want to tell you is that it doesn't mean that I have the perfect answers, and it doesn't mean that I get it right, and it's work in progress. So I think I've become more mindful of looking more carefully at the expected IRAs.

But, and I cannot say that I've never done it. It's more about doing it a more systemic way in comparing them, but it's still very, very tough. You know, so expected IRAs is an interesting one because I was talking about this with some of the other day. I've gone back and forth so many times on it. My issue with expected IRAs is what I found is when you do it by expected IRAs, and there are other ways to modify this, but it would push me to be into the most levered stuff out there because the most levered stuff is what gives you the highest upside, right? And that could be the correct answer, right? Like Todd Westler, who is one of Berkshire's portfolio manager, everybody talks about his

IRA, right? His IRA was two or three bets, super levered companies that survived and went up 100 debts, right? So it's possible that we should be taking advantage of the convexity of non-recourse leverage in the stock market by buying companies that are super levered, right? But what I was finding was when I would do a force ranking by IRA. And I mean, at some point, I would cover like 50 companies and I would do like literal, hey, here's my fair value. Like I sound like a sell side analyst. Here's my fair value. Here's where the stock is. What trades at the biggest discrepancy? And what? What I noticed is, oh, crap, all of a sudden, my portfolio is just the most levered companies I follow in general, right?

So that's why I've gone back and forth on IR and then I had somebody say, hey, we'll charge yourself a volatility factor, right? Or you can do it on an unlevered basis, right? Judge everything on the EV, judge everything on an EV basis and how much it trades on discounts of the EV. But when you go that route, what do you end up with? You end up, for the most part, investing in companies that have huge net cash balances, right? So you've got almost no beta on the upside because, yes, they trade their EV is 50. They're trading for 25, but it's all net cash. You're just kind of reading for the cash to be returned.

So I've just gone back and forth from so much. Go ahead. Go ahead, please. Okay. You want one-size answer as you and I. I know, I know, but I'm also just discussing and being a very handsome podcast host. Oh, absolutely. So look, first of all, stylistically, I think you and I were stylistically,

diverged the most, like philosophically. I do not like leveled companies. And remember, you and I were running informal, no recording book club that we need to resume doing. And we're discussing a particular book, one chapter per conversation. And our biggest disagreement on one of the chapters was do not invest in loved companies. For me, that was very natural. I was like, yep, I subscribed with that view.

I railed invest in levied companies. And I put the book down and never read it again. I put the book down and never read it again. You're like, that's nonsense, right? So you and I disagree on this philosophical and that's totally fine. Now, if you're investing in a level of conference, or you invest in a number of them in your portfolio, yes, you can know if you rank them all purely unexpected IRA,

you will end up indeed with having all those levied names at the top of the list. What I would encourage, but what is interesting to me, is that you said, okay, then I try to do it on volatility. But then I end up with a bunch of companies that have very little EV because they have too much cash. The volatility was you charge a volatility factor to account for that. And I struggled with that. So then I switched to EV and then you just get a bunch of net cash companies. And I think this, if I were you, if you hired me as your portfolio management code, by the way, I don't think you should.

But if you were to make that dumb decision, in that case, I would say, And you need to apply a penalizing factor to those, you will have simplistically, two variable ranking system. One would be expected the IRA. But then you need to add another one that will account, and now I will show the quotes, risk. Risk, I'm not talking about volatility here. I'm talking about the company going bust.

When I said volatility, I did kind of mean risk. I wasn't talking about stock market volatility, but please continue. Okay. So, and then you would be applying, most likely, fairly significant penalizing factor or downward adjustment because it's a levered company. If you tell me, Artem, I have two stocks. I only imagine the world, like, now we'll play microeconomics 101. This product A and product B, that's all.

There are no other products in microeconomics 101. So there are only two stocks, stock A and stock B, based on your map. And let's assume that you have really good best and really good estimates and everything. 25% IRR over the three-year holding period on each of them. I will say, fantastic, Andrew, what's the name? Well, give me a call. I want to know the thesis. So, but then I will tell you, one of them you tell me is, has zero debt.

Another one has, let's say, six times Ibida, four times of debt, and two times all equity. I will say that you are, these two appearances are not born equal. Yeah. Right. Now I don't know the exact part full. I don't know all exact those variables. But I think you need to force that and apply some kind of borderline common sense that here IRRs show my math, my Excel, my must-excel file spits out the same IRRs, but they're not born equal. And then you adjust for that.

For me, it's probably a little bit easier because I don't invest in the many of the companies. But I have another, other issues sometimes. For example, you months ago, you and I were discussing, and then I think you mentioned this in one of your rumblings without naming me, which I appreciate the anonymity. But at some point, I think you and I chatted about, do we as investors, take upon ourselves and our portfolors,

certain level of existential risks and whether we're properly pricing them. And that's a fascinating question because it can be 3% probability of something really, really, really negative happening. Like, imagine if you were selling product X and then that product got outlawed by the entire nation. Yeah.

That's a big problem. You all of a sudden go out of business, right? I'm taking intentional an extreme case. Extreme case that has happened, right? At the extreme case, that happens. I mean, this does happen. There are companies who have their products literally banned. Yeah, it will happen, right?

So, or imagine if you were investing in a natural resource company, by the way, I don't do that, in an emerging market country with not very stable government and not very stable political regime that may not necessarily respect bilateral investment treaty. Yeah. And you may own some the best, I'm making this up, okay, the best copper mine in country X. And then when they wake up, there is a military coup in that country. And now the new leader, in his ultimate wisdom, believes that they now should be running that company instead of your company that is listed in New York. Doesn't even have to be a copper mine. I think I did it at rambling on the podcast, things that you say no to. And Chinese companies, I've really struggled with because, A, I've never been to China.

the culture and, like, there's so much more online is difficult. But B, I just keep thinking of like the Alibaba and financial, like, hey, it belongs to Alibaba and then it did not. And like, how do you account for the 2% risk, whatever the risk is of the government just, forget the VIE structure and all the risk. The government just deciding, hey, this company, your assets now belong to us, right? Like, it has happened in China. And I'm just not sure how to account for that risk. Andrew, what I can tell you is that. You are welcome to build the China with me.

I'll take you there. And the second kid is on the way, but you know, you're not planning a trip. Maybe after the second kid's a little older and Alicia won't throw me out if I'm leaving her around for a 10-day trip over to China. As you know, I've been to China many times. I speak some Mandarin. My son speaks a messable about a Mandarin now, so he fixes my mistakes when I say something incorrectly, which is hilarious. So, yeah, I can take you there. I appreciate it.

It's going to take the Chinese stuff off my note list. It's going to add so much more to my research process. Okay, anyway, did you want to have any concluding thoughts here? Because we're running pretty long. We're going to, I'll say what we're going to do next. But any concluding thoughts on that or anything else we've talked about today? So going back to what we discussed briefly, I want to ask you, remember when you spoke about borrowing the risk of relying on someone else's conviction

and as a result made an investment mistake? And you spoke about there is a relatively small cap, there is a relatively small community of call it small cap managers. How many are out there, you think? What's the time in units? It's tough because there are boltons. Everyone's doing it a little bit differently. But I mean, I think there's a core group of like 50 people who have similar investment

styles, which I would say is for the most part, but not always. garpy undervalued sub billion dollars let's call it and then there are people who like are alongside the edge of it who might go much smaller much more illiquid or might have more like kind of me with maybe a more of an event or eventy angle too is what they focus on but i'd see probably fifth year like the core and i think everybody like reads their letters and everybody talks to them and all of us like catch up with each other once every three or six or nine months or like maybe sees each other at Berkshire meeting or something, but that's kind of what I would say. Okay, I got a question.

United States of America has about 330 million people, roughly. Do you think that the entire nation, and let's forget about our peers who may be based in the United Kingdom, maybe they're based in Asia, they may be based in Singapore, Hong Kong, continental Europe, there are many places in America, there are many wonderful investors there. So, but let's even put those aside for now. Do you really think that the entire nation of 330 million Americans has only about 50 people, 5-0, who do small-cap investing in the way? No, no, no, no.

This was small-cap fund managers running similar styles who are chatting with each other. I think there are much, much more. Okay, what fascinates me is this, and I don't know the answer. Maybe there are not 50 people like this. Maybe there are another few hundred people with somewhat similar style, but they're not chatting very much with this 50 who you and I know. I don't know the answer. I'm genuinely curious.

And by the way, so since I, okay, let me do a plug for myself. If you are a small manager, small meaning not your size of the UN necessarily, but in terms of way you invest. like small, micro, even small to meet cap. And we don't know each other. Like, reach out to meet on LinkedIn. And you can send me it to the end. How dare you?

That's what my podcast is for. Reach out to meet up on the podcast. Reach out to Andrew and then shout to me too. Because I would like to meet other people in the space, especially what is interesting and there is this. As you mentioned, you can't meet 30s and early 40s. So what I think is interesting, there is a new generation of investors who are now, I don't know, mid-20s, very early 30s, who are doing what you and I

started doing, let's say, roughly 10 years ago, plus-minus couple of years. But this is a new generation. I know some of them. I don't know many of them. I would love to get to know them. They're younger. They are viewing the world differently. They have their own competitive strengths.

They are different from ours. Their competitive strengths are different from our competitive strengths. So, like, I'm always very happy. to meet those young investors. By the way, all the two, but older, I'm more likely to know, because they probably have been in business longer, I probably met with them one way or another. But the younger guys and women, I don't necessarily know all of them.

So I'd like to meet them, learn what they're doing, talk ideas, et cetera, et cetera. Ditto, good-do. Always looking for young podcast guests and everything, too. So cool. Let's wrap it up here. I will just, again, you touch on expert calls a little bit. We touch on AI a decent bit, but we are going to do a webinar with the help of TECUS AlphaSense, really discussing how we're using expert calls and AI in our research process, investments, and everything.

So I'll include a link to that in the show notes if anyone's kind of interested in diving further into those topics. In particular, I mean, look, I was going to say in particular, expert calls have been a big focus of mine recently, but actually both. I mean, I'm spending so much time using different AI tools and stuff. And, like, I've got so many things I want to talk with you out there, but we're running super long. So Arden Fokin, one maybe my favorite guest overall. I can't think of anyone off the top of my own one of the most popular guests.

Thank you so much for coming on. I'm looking forward to the webinar, including like, oh, Arden's got one. One more thing. There's always one more thing with Arndum. Go ahead. Oh, yeah. As you know, with me, I think it was Burford number two, number three podcast, when we're trying to desperately wrap up the podcast.

I'll say, like, Andrew, one more point. Okay, so one of points on this one, I think is the last one. When you say, oh, you're spending more time on expert calls and thinking about the best way I was doing them, et cetera, et cetera. And but you're also thinking about AI a lot. And I think the interesting thing is this. I almost think that expert call libraries and AI is the match made-on-come. Yes.

Oh, 100. Okay, L-LWR, don't spoil the webinar. Don't spoil the webinar. But it's interesting. So because it's your fault. You mentioned that, right? So it's large language models. The language is the key word here.

Where else, other than SAC filings, there is so much density in worlds. Like, what else? So like, they were kind of, again, it's a perfect match in my opinion. You were the one who told me. I mean, this alone revolutionized a little bit of my research project. I didn't say revolutionized because I don't think it let. But it sped it up. But you know what I do now when I want to read an expert call?

I mean, AlphSense does have the summary at it, but what I've started doing, if I follow company, it's got five expert calls, I toss all five into an LLM, say summarize it for me, and then I summarize that, and then if I'm still interested in the company and want to learn a little bit more, then I go to the individual expert calls and read the summary there. And then if I'm still interested in like really want to dive in it, then I go read the expert call itself. And by doing that, again, I give credit to you. You were the one, I believe, who told me this. By doing that when I'm reading it instead of like, you know, when you see, when you read new information for the first time, it's actually very hard to process. But if I've kind of already

read a summary in that case almost twice, I kind of know what's coming. I can really parse the language and I really understand. And I've found my retention and my absorption is much better. So, I mean, that is just one way. It is so perfect. But it is, it's been an incredible tool for marrying expert calls and AI together. Appreciate you giving me the credit. That's true. I did tell you that in terms of how I was using AI in expert calls. Now, what I would say, and let's conclude there, because I don't want to spoil too much about our second conversation, is this. This is a framework, which is very simple, and it's not mine. I learned about this framework from Paul Enright, who used to be a portfolio manager at Viking, and he did two fantastic podcasts, one with that side of some capital locators. Another one is invest like the best with Patty Kachansi.

I believe Patekarsanxi podcast came out first. Both are fantastic. I probably listened to them three times each over the last couple of years. And he or Poland right there, all for the simple, but in my opinion, an incredibly effective network, framework. Digging, analyzing, deciding. And what I found when I think about my own uses of AI is, where AI fits in this three-stage framework,

digging and analyzing, deciding, and where I can use more of it, where I can use very little or maybe even zero it. So what's supposed here, because that's how I will be kind of framing. Okay, great. Yes, this is a great pause. We're going to do the webinar,

links in the show notes, talking about AI expert calls. But, Arndt, this is awesome. Again, I just want to come on and, if we talk about a bunch of stuff, I just wanted to come on and improve the process with you, and hopefully I've improved, my process a little bit. It helps you improve your process a little bit. And hopefully our listeners are going to improve their process a little bit from listening

to this. So this is awesome. We'll talk soon, buddy, and we'll go from there. Thanks a quick disclaimer. Nothing on this podcast should be considered an investment advice. Guests or the hosts may have positions in any of the stocks mentioned during this podcast. Please do your own work and consult a financial advisor. Thanks.