He Wrote the Book on Why Moats Fail | Ritavan on What Actually Compounds Instead

If you want something defensible, the focus should be on figuring out if the business you have is a system that compounds in a way and at a rate that some other business you're comparing it with just cannot. And I mean, that is a much more fundamental idea that is independent of, oh, okay, does it have a morph? Okay, you deployed an AI tool successfully or you adopted an AI tool because I think the big dashboard API now is AI adoption, right? But adoption per se is meaningless. It's not in agility, it's not in speed, it's not in, you know, it's not just in moving fast, right? It's about having an understanding of the game. You're watching Access Returns, the channel that makes complex investing ideas simple enough to actually use or better questions, lead to better decisions. I'm Matt Ziggler, I've got Kai Wu of Sparkline Capital here with me as my co-host. And our guest today is Investor, Speaker, author of Data Impact, and now we're really excited to talk about with him today, System Gambit, Rida von. Welcome to Access Returns. Thanks, Matt. Great to be a long time coming, my friend. So most investors, and we talked to a lot of investors, especially especially when there's a quality bias. And let's be honest, it's embarrassing to say we like low quality crappy things. So we talk about quality, people have a checklist. And one of the most common items on the checklist is does this company have a mode, brand, switching costs, network effects? You've got this book that says that question is almost besides the point. What's the test that you'd actually run on a company instead? Yeah, I mean, so about the mode, it's, you kind of picture as if it's, you can sort of measure the thickness of the fourth wall or something, like I think that's very often the image. And it makes it sound as if you're standing in front of a fortress, you need to get out your measuring scale, and then you write down, okay, it's that many meters and that's done. But it never is like that, right? And, and so I don't really like, I think, I don't really like the idea of having the mode as a, as an entry on the checklist, because it's not something you can just take off. I think what is more important is if you want something defensible, the focus should be on figuring out if the business you have is a system that compounds in a way and at a rate that some other business you're comparing it with just cannot. And I think that is a much more fundamental idea that is independent of, oh, okay, does it have a more mode or is it growing or whatever else, right? I mean, it's a perfect explanation of where you're coming from on this and part of why we wanted to talk about this. This is what sets your concept of this apart. We don't normally talk about modes like this. I want to get into a system gambit and I want you to define it here at the top, because it's not just this make a sacrifice now for the payoff later. It's not that traditional chest gambit definition that jumps out in most of our brains. What are the three conditions that have to be true at the same time for something to actually qualify as a system gambit? Yeah, so just to take a step back, right? The, the, the wood gambit is from chest and in chest, what you do is you sacrifice the pawn or a, you know, you sacrifice some form of material to gain a positional advantage that then allows you to either win the game or dominate the game. And the system gambit is essentially generalizing that idea to systems and hence, you know, to business and investing. And the idea is the following. You are in a system, but you want to cross into a new system, right? So that's the difference with chess. You're not in the same game. You're trying to go in a new game. To get there though, you need to sacrifice something in the current system. You want to gain a structural advantage or you want to build a structural advantage that then allows you to compound in this new system. And there are three properties that are super important. The first one is a self-improving loop. That means every iteration of that loop has to structurally get better. If that's not the case, you have a nice asset, right? You just have a nice asset. You don't have the compounding. The second important thing is path dependence, meaning if it's something that anyone else with just more capital can buy on the market, then you don't really, then you haven't really executed a system gambit because it's something that someone with more capital can acquire later on. So it has to be something that is built loop iteration by loop iteration. That's the path dependence. And I think if you have these three, these two things already, right? You're in a, you're in a very good place. And the third thing, I think, which is super important is management logic antagonism. So what that means is you are doing things in a certain way that if anyone else wanted to copy you, they'd have to stop or break what they're currently doing. Right? Once you have these three things, you have executed a system gambit successfully. If any one of these is not missing, then it's not a system gambit. It's something else, right? Sometimes it can either be a good asset. It can be a wild bet. It can be a bunch of things, but for it to be a system gambit, you need all three. So the gambit aspect, you're saying it's like a chess analogy sacrifice upon to take a queen. But the key difference is that it's not the same game. You're switching game, just switching systems. Correct. So is the sacrifice in this case the cost? Is it simply the switching costs? A moving from system made a system B? Or are there material costs in addition to that friction? The goal is to be willing to sacrifice into a new system. And sometimes that requires a whole bunch of things that are non-material, but it could also be material costs. But I think the important part is you're choosing across from the system, you're into a new system. And the sacrifice is often very quantifiable in the existing system. So if you're operating in a certain system, you have metrics to quantify how you are performing in that system. And in a very large way, the sacrifice is often seeing those metrics collapse. Precisely because you're moving out of that system. That is white hoods. It's not so much, oh, I need to buy this new thing or whatever, it's much more I am performing really crap in my existing system. And that is what is very hard to do for people. Interesting. So I sit here as a public markets investor. So I'm not inside the walls of a company. I don't get access to all the private information and stuff under the hood. So from the standpoint of a public market investor who's looking at companies and stocks, how can I assess which companies are first of all attempting to execute this sort of gambit? And second, which ones are positioned to potentially successfully do it? You mentioned these three prerequisites. What sorts of metrics, data or what not would someone like myself look at if I'm trying to determine in real time. Because obviously we can look at historical examples and say, yeah, we know with a benefit of hindsight that company X successfully did this. But in real time, how am I supposed to determine which companies are in the midst of executing this transition? Yeah. So I think looking from the outside to most important thing you want to ask yourself is how hard would this thing be to replicate? Because the core idea underlying the system gambit, and just hold it up. So it's a pre-launch copy. So it's a bright blue cover. So it wouldn't be hard to find. But the subtitle is that it's about finding leverage to unlock compounding value. And the idea of leverage, it's not financial leverage, obviously. It is leverage in the old, sort of archimedes sense. You want to get the most impact or value with the least amount of effort or pressure you apply. And the way you can try to figure that out or reverse engineer this from the outside is to ask yourself how much of this is leverage based on asymmetric proprietary strengths of the company. How much of this is being done in a way that others cannot just copy. I'm pushing this rid of on excuse me for a moment as I point this out to our good friend Kai Woo here on the show with us because Kai last couple of years like the code is not the mode for software companies. I've heard this from you once or twice or 30 times that the underlying tech matters less than whether they've got brand human capital network effects, whatever around it. Any as you're encountering these these three phase now system from rid of on. What's that make you think of? I'm just curious. Yeah, I mean, I think what I'm thinking about is this which is the the mode go back to that analogy. The mode is you know is dependent on the game you're playing right. So it is certain setting for example code can be a mode right. If you're the only person who possesses the ability to the code, then you have a mode or you as a company have a mode. You know, the obvious analogy today is that you know the world has shifted into a world to where AI can write code for cheaply and quickly. And so anyone can vibe code or has access to advanced coding tools. So the game is changed. And so perhaps that mode is no longer a mode in the world of AI. And I think you know as we step back just from this one particular case, you have to be very thoughtful about what game are you playing? In other words, what system to take your language rid of on is currently the dominant paradigm. And then the question becoming is what is a scarce asset? What are the modes in that setting? And I think the contextual, the context matters a lot. And so I think what's really interesting about your work is it kind of forces you as an investor to think through how is the world changing and what other systems are available to businesses, you know, both do both do technology and other factors. And then you ask the second order question, which is for each company as you kind of go through the list, what assets do they possess today or are they optimized for the system air system, the because obviously as an investor you're looking to kind of front-run change and look for companies that might be priced based on the past paradigm, but other investors in the market might be missing how their assets can actually be quite valuable in the future. So we saw the other day, I think it was getting, they're stock re-rated because it was limited to you. I think with one of the AI labs, but the idea just being that they look proprietary data could be really valuable for trading. So that's information that was of course available and valuable in the past, but perhaps it's even more valuable in a world of AI work and be used as an input to an AI model. So I think looking for analogies like that, situations like that could be a helpful framework for investors. Let me actually just jump in with a historical example because the more toward obviously comes from Ford dresses and protecting a physical Ford. And it's interesting, historical setting where this is during the Ottoman Empire, there's an Albanian prince who's been taken young, George Castellotti and he's sort of trained by the Ottomans becomes a cavalry commander, they give him a Turkish title which is Candle Bag. And then at some point they send him back to run that part of what is now Albania, but that region back then. And at some point he figures out, okay, I think I'd rather fight for my folks and breaks away from the Ottomans. And that region and all the regions around what was the Ottoman Empire then really struggled with the Ottomans because they just had these vast armies and they were able to marshal these resources and people couldn't hold out against that. And what Candle Bag then does is interestingly he's got his main fortress, the Ottomans send a massive army, they siege to that fortress. And now comes the paradigm change because it's the same fortress, the same armies, everything is the same. So if you go by your checklist, you've ticked all your boxes and actually the outcome is very clear which is the Ottomans eventually, you know, I have a successful siege and they take over the fortress. And Candle Bag does now, it suddenly invirt the paradigm right in some way. So he says, the fortress was seen to be like it had to have a big moth, tall walls, etc. And the idea was you lock yourself in the fortress, you let the army, they siege and you try to outlast the siege. Candle Bag instead leaves a very small garrison inside the fort, gets the hell out before the Ottomans arrive and hides in nearby forests and hills. The Ottomans now they siege and now suddenly the vast Ottoman army that can actually be modularized and moved around, etc. on an open battlefield is fixed around the fort. And then he just keeps harassing them night after night after night. And I think this is exactly my point which is if you have a checklist, a checklist is a list of binary items that has been abstracted out from a causal model of how reality is supposed to function. If you change that causal model, right, if you change the rules of the game, if you change the game itself, that checklist per se is useless because the checklist is just an artifact. And I think this is this is what I think the sort of core thrust and contribution of the system gambit is is stop trying to operate and look for patterns at the level of the artifact. And go down to from first principles and look at the system you're operating in because that is where the opportunities are. And it might not always be visible right from the outside what a business is doing, but I think what you can do from the outside is just ask yourself if you were in the issues, what would you do, what do you see? So do not operate at the checklist level, go level down. And if you're if I go back to how the whole checklist thing exploded and became so popular with investors, so I think it's mornage, papaya, who at least as far as I know started popularizing it in my world, I'm sure a lot of people were doing that too. But it based on at the guvande's book, right? So I forget which year, somewhere on 2008, 2012, somewhere around there. So at the end of high school and I remember how excited reading it then to and what at the guvande, you know, who's a who's an excellent surgeon, essentially, observes is a surgeon's pilots a bunch of people are using checklist successfully, right? That's his core thesis. But then you have to ask yourself, what is the causal model on which a pilot operates or an aircraft operates? What's the causal model on which the human body operates? And all of these are based on hard science, moralists, right? So the human body does not change based on someone's social media post, right? The physiology of the human body doesn't change to a large extent as far as surgeries are concerned, right? The same with aerodynamics, right? The food of the passengers does not change, you know, the turbulence outside, et cetera. And so the causal model is what is really the underlying sort of source for that checklist. And if the causal model is fixed, then it makes sense to rely on checklist. But when you have the opportunity to either change the causal model or see it change, et cetera, then the checklist as an artifact itself should be questioned. Take apart good mode versus good narrative. Because I think what's interesting here is we have the checklists, we have the stuff we can point at, say, here's the data, here's the checklist manifesto version of this thing. But then also sub fascinating point. Being mortal, the later book by the same author there when he talks about grappling with the health of his dad, I think those books belong together, personal opinion. So anyway, good checklist versus the actual narrative for a vote. How do you separate those two things? Because I think you're dancing right on the top of it here. So I think the narrative question is, is for me independent of a specific, of a specific structural aspect of the system. And I think you can take anything and try to get the best possible narrative out. So to remove the, the narrative out of the picture for now, speaking of the moat, I think the important thing is to ask yourself with the same, like if we go back to that example, I just use, you know, with a small Albanian army and the large Ottoman Empire. And I have a bunch of these in the book rights. I'm trying to use those that are not in the book. You know, the book is just full of, you know, example across history, across the world as much as possible. But the idea is the following. It's always the same resources. That's the same army. One is bigger, one is small. It's the same David, go ahead, kind of setting. But the idea is, how do you win despite not having the most resources or the largest size, et cetera, and all through history in every aspect, whether it's port of business or music, whatever, right? You keep seeing this thing happen because otherwise logically, if there were no asymmetry, the largest guy should always win and converge to a point where there's only one large thing, right? So clearly, there is a mechanism in the world that allows you to leverage asymmetry, right? To go into a new system. And then that new system has a structural compounding loop, right? With self improvement, with path dependence and with management, logic and antagonism that allows you to really diverge, right? So the original system continues in the direction it was going. The new system goes in a different direction. And those two aren't really compatible, meaning you cannot have this and that at the same time. And that's why the sacrifice, right? So you go back to this GHK3 or Scandinavian example, Scandinavian could not fight in the Ottoman paradigm in which he was trained by staying in the fort and defending the fort based on the moat and the walls of the fort and win. He had to sacrifice the fort in the sense of, you know, put a much smaller garrison, let the Ottomans lay siege to it. Those guys probably had a really bad time inside the fort, the poor guys from the smaller garrison, but it allowed him to win the war, right? So you have to sacrifice something. So you know, to the Ottomans, he looks stupid. They say, oh, these guys are such a loser. He's run away. But he doesn't mind being called a loser short term if at the end he wins the war. And I think that is the point very often David with the small sling, right? Aiming at goliath looks like a fool, looks like a loser, but he's precisely using that asymmetry. And that's the, that's sort of, it's almost like a celebration of thinking about leverage and asymmetry. And this is what the system gambit is about. I always a fan of the underdog. So I like that. And I think you make a good point that if it were always the case that the big guy always wins, then we'd have one company, right? And so what did you say reminds me of the war? me of, you know, it'll work by, which I'm sure you're familiar with, of Clay Christian just in and the innovators dilemma. Like, why is it that the big companies don't do the gambit themselves? I'm sure there are things holding you back. Can you maybe talk to that a bit? So, I think Claycust has described a very specific kind of situation right where you have a large incumbent with high end products and then you have a, you know, new entrant with a low quality product. So, it's a very specific description of a similar mechanism, right? The, what I tried to do with the system gambit is to say, okay, you know, Claycust is in one example in a concrete, you know, sort of market setting. What I had to do is if we take a step back and think from a first principle systems thinking perspective, what are all possible gambits? Like, can I think, and I think, uh, Kai, you would resonate, I'd always try to find some kind of eigenvector basis, right? Can I find a bunch of dimensions that are as independent as possible? And obviously they're not all orthogonal. They're all independent, but they're not all orthogonal. I would have ideally had them or talking. And the idea is, what are these mechanisms, right? That underlie these type of phenomena. And the book has eight such system gambits, right? And you spoke about, um, changing the paradigm, the goals, et cetera. So, the first system gambit is actually paradigm change and goal displacement, right? And so each gambit focuses on one mechanism that does not mean that you have to execute them independently. In fact, some of them have strong relationships or causality involved. But the idea is I'm giving you eight ways to look for asymmetry and leverage asymmetry. And then, you know, obviously based on your context, based on your setting as an investor or operator or both, you want to ask yourself, you know, what, what, what will give me, you know, the highest sort of the greatest bang for the buck, right? That, that's, that's the question to ask. So it's not one of those close your eyes, you know, follow and tick three boxes type of thing because I think that's just trivial. That's a farce that, that's not how things work. In your book, you talk, do you tell the story of the microscope? Um, and, you know, so over two centuries, it generated what you said, quote, visibility without understanding end quote before anyone really had built the standards around it to interpret what it was that the micro microscope was showing them. Um, so talk to me about this metaphor. Like, why is this the right metaphor for thinking about how a lot of companies are now using AI and AI dashboards today? Yeah. Um, so the, so the microscope is actually a very interesting example, right? It's, it's a mad, exciting technology when it gets sort of invented, right? So it's a Robert hook in, in what's the, what's, what's England today? And Anthony fund a living hook in, in the Netherlands, around the same time they come up with pretty different devices, different looking devices, I don't know, principle is more or less the same optically. And what happens is now suddenly the entire world, not the entire world, but like, let's say, many people who could afford that could see, uh, microscopic stuff. And so everyone is looking at these things, you know, little things jiggling and structured and they're drawing and, and, and hallucinating, right? I mean, that's what they're basically doing because what happened is you could have the same sample, you could have two people looking at it through the same Microsoft at two, uh, microscope at two different points in time and see something completely different. Um, and, and it's a bit like, you know, with a lot of gen AI today, like essentially you and me can send the same prompt to the same model at two different points in time and get two different answers because these are inherently stochastic, uh, systems. Um, but what happened with the microscope was in fact that lighting was in standardized, that, uh, the preparation of the sample that was being studied was in standardized, et cetera. And those things had to be sort of sorted out. Uh, but even when that happened, the bigger problem was there was no causal understanding of how, um, you know, how, how, how physiology worked, how human physiology worked. And so even though, uh, over time you had accurate visuals of stuff, uh, what would you do with them? You know, so clinic, so the microscope, uh, starts out as an exciting technology for about 150 years, you have hallucinations. Um, after, it's after about 150 years to about 200 years, you have new techniques that come out for standardized lighting, standardized sample prep, et cetera. So at least, you know, there is objectivity in what you're seeing, uh, but there is still no revolution in clinical microbiology and then physiology because there is no, like there is no scientific model to understand that. And this one pretty viral when I shared this article is before the book came out sometime last year. And a friend of mine, uh, you know, the PhD in math, I think from CMU or Duke, I forget smart guy, et cetera. And, uh, I shared it with him and he, he just shot back on WhatsApp saying, yeah, cool, but what about the telescope dude? And I said, yeah, true. Actually, let's dig in, right? So I hadn't thought about it. So they said, okay, let's look up what the telescope is. So again, now from first principles, the microscope and the telescope are essentially the same technology, meaning it's two refractive lenses in a tube, right? Except unlike hook and live and hook and and and and and Robert hook in in England, um, Galileo Galilei points it at the stars. And within, there's, you know, within a super short period of time of a few months or years, you have a complete revolution in astronomy, right? The, um, our understanding of how the universe works as a species just changed overnight. And the question is the same technology in two different fields. Let's just abstract abstract and abstract and call them systems, right? So you have the biology system on one hand and you have the astronomy system on the other, the same technology, essentially the same technology in two different systems to very different outcomes, right? One stagnates and hallucinates for 250 to 100 years. The other one has an instant revolution. And so that's, I think the point is the tool is great at whatever level in the sense of we're always obsessed of or this model could do that. That model could do something else. I mean, I'm not saying it's not interesting or not relevant, right? But that's not the end of the story, right? So irrespective of what AI or any algorithm or model of technology can do, the question you want to ask yourself is, how does that like, what's the system in which it's operating and comes back in with what system does it come in a content and how do that system need to operate so that you can create value? And now is what's interesting is astronomy, right? Had a causal model, right? Which was the geocentric model, right? So everything is turning around the earth, et cetera. With the microscope, you have an observation that directly disproves that model. And now suddenly you have the question saying, okay, then what's going on, right? If it's not what we thought. Nice thing is you had, you had Kepler and Kepler's mentor, right? Tyco, Brian, noting down observations, et cetera. So you had a large data set. You had a smart guy who fit a model on that data set with Kepler's model of the world. And now suddenly you had, you have this quick iteration between observed reality through the two. That's empirical. That's very fiable. And that's trustable, meaning Galileo, Galilei could look at the moon sitting in Italy and astronomer sitting in Paris could do the same thing. They would see more or less the same thing, right? They're not seeing two completely different things. And you have everything needed to improve the causal model. And when something does now work in the causal model, you tweak and change, et cetera, until reality and the causal model essentially are as accurate depictions of each other is possible. Right? And microbiology doesn't have that. And I think that is the point in business is you want to ask yourself irrespective of what the technology can do in the sense of or given whatever the technology can do now. Let's not wait and think what'll happen in 10 years. Right? Willie, I have be conscious. Whatever other absurd questions people ask themselves, but ask yourself, given what can do today right now, right? What is the system in which it's operating and how does it fit into that system and how do I allow it to compound value? Because that is what you were really interested in. Kai, this makes me think of your when you're talking about the highest version environment we're in right now and winners and losers and to Ritevon's point is like we're breaking out in two different directions. We have this technology and applies maybe to tech and growth companies in one direction and hard asset and other companies are capital intensive industries in another direction creating a winner loser class in each. I'm just curious does that how does that metaphor land with you and what you're seeing today? Yeah, I think that's a apt description of the K shaped AI economy, right? We're seeing AI is driving pretty much all stock returns. I think I saw a chart was like 100% of the stock market returns have been driven by AI stocks over some trailing period, but of course it creates winners and losers. What we've seen historically in these disruptive periods has been this effect where the market will quickly look around and say, "Hey, what is perceived to be a winner and a loser?" And then shoot first ask questions later. I think what's interesting is that when you actually look at how things have played out subsequently, yes, many of the folks who are pursuing to be losers actually end up becoming bankrupt, but many actually recover. I think that's the piece that people miss. I think going back to what you're talking about the microscope, it connects to that too because I think a lot of people when they think about AI, they're obsessed with the technology itself. This is such a cool model. It's got this many buildings. in parameters, it scored this rating on this metric, like on these various tests, it's like the best model. But like what matters more than the model itself, I think, is often two pieces. So one is like the complimentary assets, like the hardness, they call it right around it. Like why it was clawed code such a breakthrough. Well, the model was better, but it was more around like the instrumentation they gave it access to. Now it's running on your local computer on your terminal. You can, it can like look at your local files. You can do tool calls, you can run Linux commands. Right. So that was really important. And then the second complimentary aspect was just the setting. Right. So you know, an AI, an AI model, we know what it does. It's a kind of a sarcastic parent, right? It kind of looked at the auto, fancy auto completes. And so in some settings, that's actually not that useful. Right. Like, you know, I wouldn't use it as my therapist, for example. You know, there's too much downside there. But when it comes to code software development, actually it's the perfect setting to be using an I tool because there's its closed loop, will it compile what not it can check itself and there's plenty of data obviously to train on. There's very little like implicit knowledge. All the information by definition is codified in your say GitHub repo. And so I think I think the analogy here just to bring this back is is it's not just the model itself. The technology itself is obviously important in any paradigm shift. But it's, you know, what are you building around it? The harnesses the complementary technologies. And then what setting you're applying it to whether it's astronomy or biology, sometimes certain fields are ripe for a revolution. And others just are not the correct place to use the tool, right? And this goes back to the idea that, you know, to a hammer everything's a nail. Do you have a cool, I might not great. I'm just going to use it on everything. That's actually not the right approach. And I think for a lot of companies, you know, to bring it back to the business setting, I think that's the mistake that that's being made. A lot of companies are like, cool. We have AI now, like, you know, this is time to pump over stop price and talk about AI disruption and AI transformation. So I'm just going to, you know, spend a much, much of money on buying AI. But like they aren't thinking enough about what are they actually trying, problems with trying to solve and is this the right tool to solve those problems? And, and Kybase just to, you know, pick two words that you said. So it's really about signal quality at the code bottleneck. I think that is the question, which is, are you measuring? Are you closing the loop, right, at the right place? And how well are you able to measure that? That is what you want, right? Because a tool in a system, you can get a better tool later on or the capabilities of the tool might change. But the question is, what is it directed at? Right? And I think that's again, the microscopic, the telescope thing. If are you pointing it at, I don't know, plant tissue, or are you pointing it at the stars, right? Or at the moon? And I think that that's really the, the, the question is, if you're pointing it at the moon, it's an objective, independent thing that you cannot change your shape, that everyone else can also point it to the lighting, standardised, et cetera. There's a whole bunch of stuff. There's just very high signal quality at the code bottleneck. And every time you develop a model, you can check and test a model. I mean, a causal model of the universe. You can check and test and get hard feedback of whether you're right or wrong. And you can't do that with a microscope initially. And I think that is a, if we just sort of take that lesson to business and ask yourself, yes, okay, you deployed an AI tool successfully or you adopted an AI tool because I think the big dashboard, KPI, now is AI adoption, right? But adoption per se is meaningless, right? Because I just, just because you're using something, you could be using a microscope or you could be using a telescope, right? And it's always the same technology again. So you can drive adoption to 100%, except the telescope is creating value and the microscope isn't. And so you want to ask yourself, have you closed the loop? Do you have high quality signal at where the loop closes and is that pointed at the code bottleneck? Because that is where you're going to capture value. Everything else is meaningless. Right. This is kind of the whole pushback to the token maxing of phenomenon, which was a brief flash in the pan where it's all really fun. It's gone. I think I think that's like a one month thing. But like it was so crazy to me. It's like the metrics should not be how much money are you spending on an anthropic? Like that seems crazy to me. At the bit of good hearts, you know, I think it's good hearts or what is it like when a metric becomes the goal, then it stops being a good metric. And I think you have a bit of that too, right? Which is be very, be very smart about what you pick as the metric because if you just pick something stupid, you're going to get awful results. It's just, you know, it's just logical. Right. You're in scents of rising waste. Yeah. Exactly. Okay. So our next question. I wanted to go to an analogy between two different historical companies. So first was Nokia. Right. So they saw the smartphone coming. They documented it and then sat on it. They did nothing. And then on the other hand, you had a sml, right, which, you know, as we all know, was very successful. So what was the difference in how these organizations were built that led to these, these wildly varying outcomes? So I think the most important thing is to say again, I'm looking at this from the outside, right? So because you ask, how do you see it from the outside? And so this whole thing is based on what I saw from the outside because I was not inside either of these two companies. So let's start with Nokia because if you see press releases by the, you know, from the CEO, they're kind of strategy documents and investor call, et cetera. You see their focus is the following, which is we want to be as close to where things are happening. We want to log, measure, document as soon as possible what's happening. And then we will adapt. That's broadly that pitch, which is, you know, the sort of lean agile, whatever, just quickly change, like when things change, you try to change as fast. Right. So you try to align your clock speed to change. And what happens there is then that, you know, they're in Japan, they see what's happening in Japan. And they see what's happening later on with the iPhone, except you can collect the best possible signal. But if you do not have a meaningful causal model of how you in your system, meaning in your market with your customer, et cetera, et cetera, like what are you going, like what is the game you're going to play with that signal? If you don't have a causal model of that, if all you're doing is reacting to someone else's moves, right? Or the Japanese player did this, let me try to copy. Or the iPhone does that and me try to copy. If that's all you're doing, you're not really playing a game. You just get a monk, like you're aping what others are doing. That's not that cannot be a winning strategy. And that's what kind of what Nokia did during that phase. And it's pretty well documented. Like it's just, you know, you have all these interviews with the CEO and they're really selling the story of it's all about speed. It's all about agility, et cetera. And they were amazing at speed and agility. Like if you see the way they're reacting, commenting and documenting what's going on, it's perfect. The point is you don't have a causal model, right? It's like if I see LeBron do something, I copy that. Then I see Embape do something, do something, I copy that. What are you playing? Are you playing basketball? You playing saw like what, you know, what do you wait? What's what do you turn a chief? That's not really. Yeah, that's that's that's I think the absurdity of agile, right? You can be very agile, very dynamic, very, you know, fast moving, but it's not going towards any meaningful goal based on some called causal model of something of a game you're playing, right? Of a system you're operating it. Yes, I'm on the other hand, very interesting because and from, you know, from this system, gamut angle, because a lot of like you see this insane technology, right? Or like almost like a portfolio of technologies like every machine is like hundreds of millions that in the most expensive ones around 400 million or something like that. And you think, oh, okay, these guys, you know, they're heavy and R&D, which they are, they're producing a super advanced machine. They essentially, their capability, core capability is producing them with like, you know, manufacturing that machine and then selling it, right? That's their core business model. I think what's underappreciated is the fact that these machines are so sophisticated and subtle and sensitive that you can't just like sell it to TSMC or sell it to Samsung or sell it to Intel and then, you know, they just magically start producing. I mean, you're not selling a kitchen knife or something, right? It's just way more sophisticated and sensitive. And so what happens is the real capability that a SML has built is a causal model of how their particular machine in a particular fab operates on a particular design at a particular point in time with a particular set of environmental conditions. That knowledge, that institutional knowledge, that is what's valuable, right? And that is unique. And that's how I mean, I think the underappreciated thing often in businesses, there is no easy way to put your understanding of the cause of your cause of your causal model of your system of the game you're playing. You cannot put that on the balance sheet like there is no clear way to financially map your understanding of the game and the system on a balance sheet or on an income statement or something. And I think that is why a lot of people aren't incentivized to do it. And that's unfortunate because that is where the edge lies. And you see, like, all games, right? I mean, the smart, I mean, there's just this recent clip with Messi where he's barely running because you want to be at the right, like once you understand soccer, well, you want to be at the right place at the right time and do the right thing. It's not about how much effort you put in, it's not about how many steps you clock, right? It's not a, those are the rookie KPIs, right? And so you can ace the Rookie KPIs, but the question is, do you want to be doing that? And is that going to help you win? This P less Aping, be more Albanian ending, something like that. No, build a causal model. I think the really big takeaways in every field, right? Whatever if it sports, if it's business, if it's, if you're building an empire, if you're trying to win a battle, whatever it is you do right outside of business, you're always building a causal model. And the one with the better causal model has a better foundation to do all the other stuff. And I think this is a very under-appreciated thing very often in business, both from an operating and from an investing perspective. Well, we're looking at, we're looking at those companies though, I have to feel like you're making that assessment and back to the Albanian scenario, it looks like you're losing. Like you're making the investment, you're deliberately not playing game A because you're trying to play game B in the new system. And by deliberately not doing that, it's hard to look at that and discern between successfully playing a new game, playing a failing strategy. Yeah, and I think I think there's a third angle where, you know, if you had one of these short sighted private equity players, but, you know, sort of a quiet ASMR, I think you could really cut a whole bunch of stuff and max out on EBIT for three months or, you know, for a quarter, perhaps a few quarters and kill the company. Right? I think that's very much also a common playbook is you just, you know, the first thing when you see a golden goose is you slaughter the goose and you sell the flesh. That's the first move that every pathetic operator really does. And it looks amazing for the quarter. The question is what happens after? And I think the smart move is, can you take a step back and figure out how you can get that goose to lay a golden eggs? I hopefully had some of those eggs to get more golden geese. That's the game. And I think this is to your question, right, Matt? It's do not look at the look, don't look at the financial value of one as being sold, right? Because if I sell the flesh of the golden goose, I might be able to get a much higher pay off than if I just sold one or two eggs from the golden goose, right? Question is what are they, you know, what is being like, what is that drop? What is driving a certain gain or drop in financial outcomes? I mean, that matters so much more. Now, if we just take another example, you know, from agriculture, something, if you have, if you, I don't know, inherit or you get a, you buy a field which has crops on it, right? The first thing you can do is just cut all the crops and sell them right now. But then next season, you're essentially going to be broke. And so the question is much more, you know, what is that compounding loop, right? Have you figured out a causal model of how your, you know, what your field and agriculture in that place can do? How do you max out on that? Where's the hidden leverage, right? And what is your causal model that allows you to create value with that field, with the, you know, with the resources you have? But by being smart about it and doing things differently and not necessarily first, just cutting and selling everything which would be a typical, you know, one quarter private equity play or, you know, or just doing what the farmer beside you is doing. And I think that is, those are the two defaults where people go to and those are problematic because I think there is a whole range of other things that you can do. And it's just intellectual, I would say, you know, laziness not to explore. Yeah, I mean, I think that's exactly right that, you know, optimizing for short term performance, whether as an operator or if you're an investor looking for companies that have, you know, traditional quality metrics, oh, you know, we have high and stable EPS, right? Like that can oftentimes miss the long, the J curve, right? We're most, so for my standpoint, intangible investments, this is going to wear a lot of my work focuses. They tend to be interesting because and underappreciated by many investors because they have this J curve profile where you spend the money on the R&D for ASMR and it doesn't actually lead to a payoff in the next say year or two. And in fact, they actually hurt your earnings because you're now spending resources to invest. And it shows up over the next 10 years and perhaps, you know, it's what allows you to be such a great company. But I think for a lot of investors, they see that they don't like that. That's like a negative attribute where it should really be positive. And I think what's interesting about your work is you're taking that one step further and you're saying it's not just making investment, it's making investment in finding the next, what is it, whatever the next paradigm or system is that can generate this compounding. So it's almost like a super investment. And but it all goes down to the same underlying concept, which is this J curve or like, you know, trying to defer, defer the rewards, just the idea of investing. Yeah. In many ways, I think with the ASMR also, you would think it's a production company. They're producing these machines and selling them. In many ways, actually, what they're doing is some form of contracted delivery, right? Basically, they're guaranteeing your fab, like, you know, if you're a TSMC or an Intel, they're going to guarantee that you will be able to produce this many chips per hour at this level of quality because it's their engineers embedded on the ground to make sure their machine can deliver that. And I mean, that is something that in that, I mean, it's irrespective of how much, let's say they get paid in terms of service fee to do that. I think financially, irrespective of that, just the fact that they've got boots on the ground and know what's happening in the fab, that knowledge institutionally is impossible to replicate. There's no way. Yeah. We're seeing a resurgence in the four deployed engineer these days. And I feel like that's the same model, right? It's not the same. You know, correct. The SML did it probably, you know, 50, 60, whatever years before Palantir and they didn't spin that good story. I think that's the. I mean, they're not necessarily dominating the narrative, I think. And that's that in the point is, is, is, if you only look at the current financial statement number of something now, that just misses the opportunity that the system gambit, I think gives you. So I think you successfully rejected the business book checklist, which I think I applaud now. I mean, I applaud it a lot. I don't know. I wanted to talk to you about this book because you gave me so many examples of non financial history stories that apply strategically to how we think about corporate strategy, how we think about what does RLI look like in an age of AI right now? I'm just curious how, how, how conscious was that as a choice? Did you know you were going to go into all these weird historical places when you started the book? I said, yeah, not at all. You know, so I think it's, it started out like my previous book, data impact at a six step framework and the second step in the framework was the word leverage. Right. And a lot of readers came back to me and said, he got us excited. You know, you've, you've now got us hungry. You, you know, you serve the starter. Now where's the main course? And I wasn't expecting that, that, that kind of reaction. But then, you know, that really, that really, that really kind of, you know, I had this, you know, this, this each then and I had to describe because I felt I did not have a, a deep enough answer that I was proud of. Like what I would, you know, what I wrote in data impact and leverage chapter was, hey, focus on, you know, your, your unique proprietary strengths, focus on your non physical, as your physical non digital assets, focus on your brand, on your reputation, on a whole bunch of things that, you know, typical business books also have. And I said, hey, you bring these together. You know, tangible and intangible. You combine them. You leverage them and you create value. But I mean, it's, it's, I think anyone could have written that. I mean, I felt like there wasn't that unique take on this. And the question was, what is the mechanism? Because I can have all these assets like you could, you can picture two companies who have, you know, this same strong brand, who can have the same intangible other assets, who can have the physical footprint of stores or whatever, right? So they can have all of this. And yet you can get two very different outcomes over 10 years. Right. And so, what is the mechanism that allows you to take all of this and leverage it successfully? And, and from that, back from, from that starting point, I think it was really a journey of just curiosity and looking around and not shutting stuff out. So like not digging into and within just the sort of narrow business literature or field. But to say, hey, if something has a certain universal characteristic, like, can I find an underlying mechanism that appears everywhere, right? And then you know that you unlock something that's meaningful, something that's durable, and that's something that, you know, that has a certain truth to it, right? That is not just, oh, I just, you know, back, I kind of overfit these three data points type of thing. Because I think that's easy to do. And a lot of playbooks and check, checklists and a lot of that is typically this. And I think you can sell and hustle those type of things short term. But I think if you want really unlock hidden structure, you just have to look more widely. And you know, one thing just kept leading to another. As I traveled, met people, saw stuff, I just every time I'd take a step back and say, what is the underlying mechanism here? What is the system? How has value been created or not? And where is the asymmetry? Where is someone able to do more with less resources? And then you just keep seeing these patterns all over. And, and then you just have to kind of, you know, order your thoughts. Find commonalities in those patterns, extract kind of these principles and then just, you know, package it into a book. And that's probably, I think, the work I've done. The last chapter of this book. And I think this is so important for where we are. right now in the, you know, we're, we're post the SpaceX IPO. We're in the middle of whatever's going to go on with that. Or before anthropic, hope in AI, whatever these other companies coming public, it is about to happen. We're in this boom of money coming into the market to develop these ideas. We're going to find out who the real Albanians are. That's basically what I'm thinking. You still feel like you have a pretty contrarian take. I'm thinking of the last chapter in the book. Can you lay out that thesis? Lay out what you're seeing right now where we are. Yes, I think the core point is the following, which is that in many ways, AI is a general purpose technology. But for the first time, it's a general purpose technology that you can bolt on to whatever your system is. Right. So whatever paradigm you're operating it. So, you know, if you're an industrial business, then you sort of bolt it on to your industrial paradigm and you, you know, you build dashboards and you try to predict and measure stuff here and there. Right. If you're a digital company, meaning like a software company, then, you know, you apply it within that in terms of how can I validate a use case faster, et cetera. Sure. A platform business. You apply that to increase interactions to tweak your recommendation algorithms and a bunch of stuff, right? And so the core idea is the following because the data that you have is generated through your existing business, through your existing system. That data by definition is within system within paradigm data. If you bolt AI onto that, you're essentially training within paradigm within system. And the core idea of the system gambit and especially the first system gambit is about paradigm change and goal displacement. That is where there's opportunity, right? Because if you stay within the same system, you're again playing the resource game, right? Who has more resources? And then you know who has more resources. If you want to get asymmetry, you have to see differently. It's the same system physically, perhaps, but you are take on that. The paradigm you choose to play in at the game you choose to play in changes, the outcomes. And I think the biggest danger with AI and AI adoption today is if you do it in a naive way, if you do it in a way that anyone with a credit card and an API key can copy you, right? Then where is your edge? And more importantly, you've just increased your cost without any knowledge or without any understanding of how you're creating more value. And this within paradigm optimization is exactly how you optimize yourself to its relevance. And especially if you're one of the big large players, you need to be extra careful because it's usually the underdogs that find the leverage and that eat your lunch. So then what is the, what is the system gambit in this case? You're saying what it's not? It's not stay within this existing paradigm, slightly optimize your business using AI. If it's not that, what is it? So it is defensive, right? So if you if you see someone do that and you feel okay for some, you know, they have already, you know, optimized within paradigm a little bit and I don't know, increased a bit by 1%. Sustainably, then you know, like you can, you be a follower there. Right? Don't, don't try to lead that trend. Just see what people are doing. And then you just follow because essentially you are letting them de-risk whether something works or not. And that's actually why I call the system anti-gammon. So if you only do this, right, then you'd kind of doing the opposite of what the gambit should be out. Should should be about in a system gambit. And again, there are eight system gambits in the book. It goes one by one through each of them. And it gives you, like it gives you essentially like you're, if we go into fitness and health, I'm not proving anything. That's what you could always say. Oh, you're using mainly examples that work. And so, you know, there is no, like you're not really proving the point, etc. But I'm thinking I did much more from a practical perspective. I'm a bit like a fitness training trainer giving you a bunch of reps. You can do and variations on the exercise. So each gambit is think of that at like a composite muscle group exercise. And I'm basically saying, Hey, you can do the squat this way. You can split your legs a bit more. You can squat on one leg or like stretch this leg or whatever. So I'm giving you a bunch of things in each chapter. You just repeat and get the reps in. And the idea is that you start then pattern matching stuff. In a way that, yeah, I cannot because the eye is trained on granular data within your paradigm. And so the idea is, I'm giving you a way to concretely, practically look for patterns based on a vast and diverse range of examples across all of the eight chapters. And with that, you will, you can then look at your specific context, right? Your asymmetric proprietary advantage, et cetera, and ask yourself which of these system gambits is for me now relevant, right? And how could I execute it? Or if you're an investor who out there, like seeing from the outside, who seems to be executing it? Because the thing with the Jacob is right, that's just it's a descriptive artifact of what's going on. The question is, who is seeing just a drop in performance or like a value trap, I guess, in the investor jargon, right? Who is seeing just this? Who's just seeing some kind of drop and it stays there and who's going to unlock a compounding loop, right? So who's building a structural condition that compounds? And then that is the core contribution here is once you go through the eight system gambits, you have eight concrete things and you've done the reps by the time you're done with the book. So you start seeing these patterns. Is there anything in your work on intangibles where you see those, where you see this methodology map over some of the historic work that you've done? Yeah, so a lot of the work I've done as a quantitative investor has been trying to understand like base rates. So for example, like if there is a disruption occurring in e-commerce and you have always broken more to retailers and some of them are trying to become Walmart and successfully navigate this, but most fail. Like what's the success rate in that situation? And then the second question of course being, let's talk about the fundamentals more about what's priced into the market. So as an investor, you pay the stock price that's available to you at the time. And so if stock prices are down, saying software stocks today, what's the chance that they recover versus not? And do we see more dispersion? And historically, the answer would be yes. Where many of these companies are zeroes and others end up being great generational buy. And then there's a stock at a P ratio of eight that goes on to become the next Walmart in its class. And the answer is a few things historically. So obviously it's the use of the technology itself. Are they adapting to the new paradigm? But it goes beyond that. I think you mentioned actually some examples of proprietary data. Obviously, AI is only as useful as the data you trained it on. It's garbage and garbage out. So to the extent a enterprise and incumbent has a lot of interesting customer data or whatever is applicable to its domain, that could potentially be a very valuable asset in the age of AI. And you can kind of go down the list. And it does turn out that like complimentary assets have continued to at least historically hold value. And of course things change. Right. Modes can have road assets brands may be, you know, maybe valuable one day and less valuable in different context. But on average, shaking across, you know, the thousands of stocks over decades, these complimentary assets generally intangible, but also tangible as well have tended to be to a decent. And I guess one question I would spin back to you is around is you know, you frame it as kind of binary. There's gambits and then anti gambits. You know, is there a middle category where it's like, hey, look, you're an industrial business. You have a great business. You're a market leader in whatever category you happen to compete in and now AI is here. Right. Like maybe it's fine for them to just say we're going to take incremental advantages of AI without trying to complete the re-orange our business. It's fine. It is a sustaining innovation. Like in the internet, the example I just gave, like if you're a retailer, then yeah, you definitely need to figure out e-commerce because if you don't, you're cooked. But you know, there are plenty of businesses that just use the email to be a little bit more productive. You know, is it fine to be in that in that final category? Like should everyone be trying to extrude a system gambit at the same time, knowing that not everyone will be successful in doing so or is it fine to kind of just not even play that game? So since we spoke about retail and Walmart, I just, I just lay out a thesis and I think that'll answer the question. Hopefully, you know, perhaps not with a one sentence, but you know, with a lot of context. So if you go back in the financial press, like about a dozen years or so ago or two decades or so ago, you had for initially the phenomenon that Amazon, like the e-commerce business was not making any money, right? And so you people writing, or this is one of these calm dinosaurs and I don't know why they're not going bust. And you know, it's not really a business. And you know, what are they doing? They're selling books now. They're selling everything under the planet, what nonsense. That's not a business model, et cetera. What Amazon is doing in the background is actually executing the system gambit in multiple paradigms, right? So, Jeff Bezos speaks about the flywheel and I think I forget who the author was, but there was this idea of the fly- Jim Collins. With Jim Collins, exactly. So Jim Collins, I think, had a workshop in the late 90s there and then, you know, Jeff Bezos really liked that and said everything we do has to be based on a flywheel. Now, flywheel is basically compounding, right? So it's like every loop, every iteration gets better. And so the flywheel is essentially a compounding loop and where Jeff Bezos did obviously is then to say, okay, everything we do has that feature, which means you have to sacrifice short term on the metrics, metrics like profitability to build that compounding. and it's like essentially they executed probably the greatest system gambit of the early 2000s and in many ways until now but like what they're doing is operating on three paradigms. So there's on one hand building out their warehouse and infrastructure which is an industrial style business which is you know you want as much predictability as possible you're moving physical inventory around you need that physical footprint for warehouses. On the other hand instead of building out the physical footprint like stores to sell the stuff what they do instead is to operate in the digital paradigm right so it's the Amazon online store right so you can order there. That is driven on learning loops right so you want to collect as much data use user data as possible to understand what users want when they order what etc so it allows you to price things dynamically etc it allows you to offer discounts it allows you to recommend products better and now comes the interesting part so you've built an industrial you know warehousing logistics business operating on data and algorithms you've built an online store front operating on data and algorithms that in itself is cool because that's the fly with two flywheel running now comes the cross system reinforcing feedback loops because I can understand because I understand you the behavior really well I can actually start shaping it right so now if I have a product that's on inventory in my warehouse I can discount it a little more aggressively right to sell that product off my end to keep my inventory to improve my inventory levels if I have a shortage of a product I can jack up prices and so now by building a cross system feedback loop right so you're taking two paradigms in which there is already a feedback loop running a compounding loop and now you're crossing them and then comes the platform paradigm where you with full fill by amazon with fba so now you open it to all sellers right and so you're now saying I already have a physical footprint I own the customer on one hand now I'm letting why should I produce my and sell my own stuff let me just offer their infrastructure to all these people who want to sell their stuff except you have leverage on them because they can't just leave like once they commit to using your stuff they are playing the rule on your terms and you can see what products are selling better and then with basics you can essentially put them out of business which amazon actually did now let's leave out the ethics and whatever you think about that it's just worth understanding the insane leverage they unlock by building within paradigm compounding loops and then crossing them and like you know a few sort of making them cross the cross paradigms and come and with full fill by amazon now you understand use of behavior you're selling someone else's products you're not limited to your own products right so that's the platform model you take your existing customers your existing physical assets and you open it up to any seller and then you add prime and prime is the layer that kind of connects all of these right so with prime you essentially tie all these three things together you maintain leverage over everyone so you maintain leverage over your customers because you know so much about them etc like if they go to someone else they won't get as good recommendations you have leverage over your sellers and that is why no player out there like a digital startup that is super fast at shipping software or at building any you know website or a store or non-line storefront etc they're not able to really ever attack and break amazon because amazon is a three-paradigm beast the traditional business that is you know a strong logistics business that says okay I'm going to knock these guys out of the market they can't do that because it's again a three-paradigm compounding beast and and I think that is something that's under appreciated and I think for the first time in the system gambit I really felt understood how I'm like why amazon is so unbeatable and now comes the Walmart part right so as long as Walmart was trying to play the amazon game so by two so from the early 2000 late 90s early 2000s to the 2010 amazon is the you know the joker the loser the you know no profit business and then people finally caught on to that and realized oh shit these guys were executing a multi-paradigm system gambit build this massive compounding machine and what is sacrificing a you know 5% ee bit for 10 years right it's like it's meaningless right in terms of what like the amount of value they built sacrificing that ebit for 10 years is complete like it's chicken shit once they have that then there's suddenly the same narrative the same people now say oh am is now Walmart is dead right so Walmart's running successfully there while amazon was not you know profit the world Walmart was putting out good profits and so Walmart was the good guys amazon's gonna go bust then they realize oh no amazon is become a beast now Walmart will go bust which is also an absurd story because anyway so Walmart now has these physical stores right as long as you say Walmart now has to become the new amazon meaning they have to set everything online that's an absurd proposition because Walmart needs to focus on their asymmetric proprietary advantages right what is it that they can leverage and obviously what they will leverage will be different from amazon and so what over the about 10 years you know Walmart is kind of aping on the outside where amazon does okay we will you know use some digital systems here and there we'll get a better CRM we'll do this that and you know these kind of small defensive tweaks what I call the systematic gambit it's not a bad thing necessarily it's just the rational thing to do in the existing system and then about half a decade or so ago you have set the barrier who's in x amazon exec move to Walmart think he joined as cheap growth officer and remember so and he was before that for a while at insta and now over the last half you know half a dozen years or so what Walmart has done is they said what is the one thing we have that amazon will never have despite having bought whole foods and that's the physical presence we know how the how our customer not just shops online on on walmart.com or whatever we see them physically shop that's that's a proprietary strength that we have that amazon can never have by design structurally unless amazon suddenly starts opening physical stores which would completely destroy them so that's management logic antagonism right and the second thing here is path dependence on the shopper that has shopped in walmart over two or three generations that's a level of path dependence that you've built a level of trust a level of brand a level of customer understanding that you've built about about a customer and a community a local community that amazon just cannot have by design so you have path weapon right and and you have the self improvement loop which now needs to be built right so two of these were already there and then what they did is okay we will now take what we understand of our customer online will take what we understand of our customers uniquely physically we put this together and now we try to delight our customer and with that they are now on such an amazing trajectory and they went multi paradigm meaning you can buy online you buy and store we have a loyalty program that's the platform model that's so you're taking industrial business with the physical stores you leveraging and building you leveraging that to build your digital capabilities and then you open it up through partnerships etc with the loyalty program and now again you've built an insane compounding beast so it does not matter where you start you could be a pre-.com bubble tech startup you could be an established family owned you know a hundred or whatever half a half a century old business it does not matter who you are and where you come from what matters is are you willing to think from first principles from a systems thinking perspective and are you willing to unlock leverage and do something that is different from the crowd that is outside the scope of the paradigm in which you're operating in a smart way so it's not just oh let's just wake up in the morning and brainstorm 10 ideas and then randomly do three it's can we rigorously think from a systems perspective understand the mechanisms and really map it out logically in as a causal model of what we're trying to do and if we go wrong or if a specific thing doesn't work out then you update the causal model so you're not just you know you're just you know just madly shooting at you know you're not just throwing stuff at the wall and seeing what sticks you you have a causal model and then you're updating or modifying it based on empirical feedback and so you're closing the loop. I'm so happy you did that because it really to me threads why thinking in this corporate strategy way matters for investors it's so interesting to be able to look at why is the company making the decision they are or not and have this framework that you're laying out in the system gambit to do so give me either the one lesson give me the one lesson and investor could take away from this book that you would hope if they read this this is what's in their head this is how they're thinking about the companies they own or invest in going forward. It's always hard to distill it into one lesson but it I think the real opportunity at any at any given level of assets at any given level of of size is the question what game are you choosing to play and the system gambit allows you to think that through from first principles in a way that allows you to unlock that leverage and it could also be on a time axis right so if everyone is trying to optimize the next quarter and play that game the question is can do you want to play on a different [BLANK_AUDIO] time axis. Right. And so concretely, you can map it on a bunch of things. It can be based on the assets you already own, physical or intangible. It can be based on time. It can be based on a bunch of stuff. And that's why it's contextual to you. And the system gambit is really, the system gambit, the book is really just like a bunch of of workouts and reps that once you get in, you can choose to play the game you want. Right. You just, you just held the better smarter person. And then you, you know, you, you, you play the game, you are uniquely capable of playing beautifully said, Ritavata, if people want to find the book, say the name again, say where they can buy it, say where they can bug you on the internet. So the book is the system gambit. So that's the, so that's the system crossover from blue to black. The book is an Amazon. And you can go to the system gambit dot sub stack dot com. That's where I share stuff or go to the YouTube channel called the system gambit for more. And otherwise, yeah, just follow and LinkedIn and engage on LinkedIn. Make sure you do that. Chase would have found out. I'm telling you, I got an early copy of the book. So the guy we loved reading it and said, this is going to be such a fun conversation. So hope you learned something, check out Ritavon online, kaiwu, sparkline capital, make sure you his work on intangibles, dovetails with this so nicely. We're going to write all this up on excess returns on the sub stack wherever you're watching and listening. Thank you. Like, comment, subscribe, all the things below that we are out. Thank you for tuning into this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcast and the excess returns network at excessforturnspod.com. If you have any feedback or questions, you can contact us at [email protected]. No information on this podcast should be construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.