The (Working) Theory of Weird Markets
Andrew Walker’s working thesis is that conventional investment strategies are increasingly dominated, leaving small investors to seek alpha in “weird” situations with few or no historical parallels. Pod shops can process credit-card data faster and with more leverage, quants can screen valuation factors systematically, and machine learning can execute rules or trend-following at scale. His conclusion: “If you want to outperform, if you want to generate alpha, if you want to be different, you have to do something weird.”
The market’s extraordinary rewards make finance Walker’s candidate for “the most competitive game in the world.” His signature comparison is competitive Rubik’s Cube: the championship winning time fell from 23 seconds in 1982 to 20 seconds in 2003 and approached five seconds by 2023; in 2019, the fastest competitor solved it with their feet in 17 seconds. If a $36,500 total prize pool can drive that improvement, finance—with Buffett’s net worth cited at $150 billion even after giving away a great deal—should relentlessly exhaust discoverable edges.
At the highest level of competition, winning strategies often look irrational to less-evolved players. Walker moves from the Fosbury Flop and fourth-down analytics to AI chess and poker: he quotes a description of machine chess as producing moves that can look “simply wrong,” while a former poker semipro told him an AI’s $500 bet into a $100 pot could resemble “a drunk uncle” throwing chips around. The investor implication is that an unfamiliar-looking process is not necessarily mistaken; it may reflect a game whose optimal strategy has changed.
Weirdness matters because highly optimized systems can remain fragile outside the conditions they know. Walker recalls chess players making apparently bad openings to take a computer “out of the book,” and StarCraft AI that was “the best player in the world unless there was even a minor surprise.” He argues that fundamental investors therefore need surprises and fat tails—not merely better execution of the same historical-data playbook.
The strongest examples, in Walker’s framing, combine a nonhistorical catalyst with fundamental thinking that still requires judgment. AI-driven electricity demand was not in historical data four years earlier, yet recognizing power as an AI constraint could make a fortune; spin-offs similarly bring forced selling, limited histories, new management teams, and sometimes liquidity constraints for pod shops. These are places where “the future” is not already cleanly modeled.
Walker treats WBD and unusual management incentives as possible specimens, but openly questions whether his evidence is strong enough. He is long Warner Bros. Discovery and sees its rare bidding war—Ellisons personally guaranteeing an equity deal and saying the bid was not best and final, with Netflix, the Trump administration, and the Warner Bros. board involved—as an N-of-one event. Yet repeatable 8-K signals such as a manager taking all of the next five years of equity compensation in the current year may already be getting “picked over,” creating both opportunity and risk. His honest caveat is that difficulty finding examples might mean “the evidence doesn’t back that up.” He is presenting the theory as a rough draft for feedback because it is central to his investing and annual work for 2026.
1. Alpha migrates toward situations the standard playbook cannot classify
Walker’s theory begins with a blunt premise: “The stock market is the most competitive game in the world.” As capital, technology, and specialization compound, traditional strategies are increasingly dominated by firms built to execute them faster, more systematically, and with greater leverage.
Credit-card data belongs to pod shops; simple deep-value screens belong to quantitative models; general rules and trend-following increasingly belong to machine learning. If the underlying quarterly edge is only 2%, a pod shop may lever it into 8%, while an unlevered small investor cannot justify the same work.
The proposed escape route is “weird”: N-of-one situations without useful precedents. Walker’s favorite historical specimen is Twitter, where the world’s richest individual decided on an acquisition on a whim, then tried to back out and claimed an MAE—one of only roughly five publicly traded MAE cases in history. The past cases he names involved the global financial crisis causing banks to fail or an individual company being hit like by a meteor, rather than the world’s richest man getting cold feet over price.
2. Finance’s incentives ensure that ordinary advantages get competed away
Walker uses measurable sports progression to show what sustained competition does. In baseball, only 11 pitchers averaged a 95 mph fastball in 2007; by 2025, 300 did. What once signaled elite velocity became commonplace in under two decades.
Rubik’s Cube supplies the sharper analogy. Championship times improved only from 23 seconds in 1982 to 20 seconds in 2003, then approached five seconds by 2023 once regular competitions organized and intensified the field.
The punch line is almost absurd: in 2019, the final year of the feet category, the fastest competitor solved the cube in 17 seconds—faster with their feet than the 2003 champion had managed with their hands. The total championship prize pool was only $36,500, with $5,000 for the premier 3×3 event.
Finance offers vastly larger rewards and hundreds of years of competition, from the rumored Rothschild racing-pigeon network used to get news from Waterloo faster than peers and Reuters—named for a man who used pigeons to bridge a telegraph gap—to firms spending hundreds of millions for milliseconds or microseconds. Walker’s challenge: given those incentives, “every edge is sought out and taken.”
3. Mature games reward strategies that initially look insane
The Fosbury Flop is Walker’s cleanest physical example. Before Dick Fosbury won Olympic gold in 1968 by clearing the bar backward, telling an elite high jumper to jump that way would have sounded ridiculous; afterward, the once-insane movement became the dominant technique.
Strategy evolves similarly. Football teams once avoided fourth-down attempts because failure could get a coach fired; analytics now favor going for fourth-and-four around the 40 or fourth-and-goal from the three. Walker invokes Moneyball’s example of hiring a first baseman who could not really hit but took many walks, illustrating that on-base percentage mattered more than batting average.
AI pushes the pattern further. Walker quotes an observer describing AI chess as producing moves humans and human-trained machines found counterintuitive, sometimes “simply wrong,” with games that look like “chess from another dimension.” Poker systems also overbet far more often than humans historically did. A former semipro told Walker that, without knowing the source, one might mistake the AI for “a drunk uncle” recklessly throwing chips around.
4. Surprise is the small investor’s remaining terrain
Walker recalls the Deep Blue–Garry Kasparov example and says Kasparov used apparently insane opening moves to take the AI “out of the book,” sacrificing local optimality to create unfamiliarity.
StarCraft AI showed the same limitation under controlled conditions. It could beat top professionals on a particular map and setup, but altered conditions broke its advantage: “The AI was the best player in the world unless there was even a minor surprise.”
AI-related power demand illustrates a tradeable fat tail. Four years earlier, historical data could not reveal the coming load, and often even future demand was not visible; the valuable insight was that widespread AI deployment “is going to consume a ton of power.”
Spin-offs offer a more structural version: unwanted shares create forced selling, standalone histories are sparse, and new management teams may offer human views AI will not have. Pod shops can participate, Walker concedes, but liquidity constraints may leave openings for smaller fundamental investors.
5. The theory is strongest as a framework and unfinished as a market map
Walker identifies WBD as a current N-of-one and discloses that he is long. Few bidding wars exist as precedents, while this one combines Ellisons personally guaranteeing an equity deal and saying their bid was not best and final, Netflix, the Trump administration, the Warner Bros. board, and numerous “soft considerations.”
He is less certain about turning the framework into a repeatable opportunity set. Unusual 8-K compensation changes—such as a manager taking all of the next five years of equity compensation in the current year as a reward—can be interesting, but AI may eventually learn that pattern too. Walker sees both opportunity and risk.
Walker says the first two-thirds—the overview, theory, and sports parallels—are very good, but he has writer’s block when translating the framework into specific market examples. He is airing this rough draft to solicit examples and objections before building his 2026 annual work around it; the difficulty may mean he needs better examples, or that “the evidence doesn’t back that up.”
Full transcript
All right. Hello and welcome to the Yet Other Value podcast. I'm your host, Andrew Walker. If you like this podcast, it would mean a lot if you could rate, subscribe, and review wherever you're watching or listening to it. And as always, if you don't like it, forget about it. Don't rate, subscribe, or review, and go on your way. See you later. Today's episode is a slightly different episode. I'm going to introduce a theory that I kind of talked about a little bit in my December random ramblings. I've been thinking about it a lot. I'm trying to write a piece on it, and I wanted to put, let's say, a rough draft of the theory out into the world to get feedback on it, basically. So, I'm going to—it's not my random ramblings, but it's just me talking about this theory. I'm putting it out there because I would love to hear from you if you've got thoughts on the theory, ways I can improve the theory, or weaknesses in the theory, because, as we'll discuss, it's kind of core to how I'm thinking about the upcoming year, investing, and everything I do. So, you'll hear it all once I get there. So, we're going to go to that episode, but first, a word from our sponsors. Today's podcast is sponsored by fiscal.ai. Fiscal.ai is a modern data terminal built for investors who want an institutional-grade platform without the complexity. Whether you're an individual investor or a professional portfolio manager, fiscal.ai gives you instant access to years of financials, earnings transcripts, and company-specific segment and KPI databases, all in one intuitive platform. What makes it stand out from other platforms? Speed, depth, and ease of use. Their data updates within minutes of earnings reports, not day. Segment revenue, subscriber growth—it's all there. Easy to chart, compare, and export. I've been using fiscal AI for interesting ways to chart and graph and visualize different segment KPIs, comparisons, all of that, and I think it's been really interesting. Particularly, it's the segment data. When you put it in a graph, you can get some really interesting comparisons—margins from one quarter to another, how they've evolved over time, stuff like that. Anyway, use my link fiscal.ai/. That's fiscal.aiyav for two weeks free plus 15% off any of their paid plans. That's fiscal.aiyab. All right. Hello and welcome to a special episode of the Yet Another Value podcast. They're all special, but this one is extra special. I'm your host, Andrew Walker, and today I have a slightly different episode for you. I'll explain in a second. Let's just start the same way we start every episode. Quick disclaimer: nothing on this podcast is investing advice. I can't emphasize that enough. I can't tell you how weird, how strange I am all the time. We're actually going to be talking about how weird I am. That's a funny turn of phrase. So, just remember that full disclaimer at the end of the podcast, consult a financial adviser, all that sort of stuff. Okay, let me explain the purpose of this episode. This is just me hopping on and rambling like a madman. I do that once a month in a segment I call my random ramblings, but this is not that.
The reason for this podcast is that every year I do a lot of annual stuff. I write up my annual outlook for my Yet Another Value empire, I write an annual letter to investors, and I do all sorts of annual stuff. I’m hitting a bit of a frozen point right now. I’m working on all of the stuff for this year, and it’s getting bigger and harder and all of that.
The central thesis of what I’m trying to write involves a theory—a theory that I kind of alluded to on my last random ramblings. I call this the theory of weird markets. I do understand how hoity-toity and arrogant I can sound saying, “I have a theory of the markets,” but it is what I’m calling my theory of weird markets. That is the center of a lot of my vision for my empire and for how I’m thinking about investing—everything I’m doing in 2026.
I need to get this theory piece, my theory-of-the-markets piece, out before I can write everything else, because I need links to send back to it to explain, “Hey, here’s the full theory, if you want.” But I’m hitting a bit of writer’s block—building it and just finishing it. I’ve got the vision in my head, but I’m having trouble getting it fully onto paper.
I thought, “Hey, a lot of my favorite writers do podcast versions of their articles where they literally just read the articles that they wrote. I could do that.” Maybe it’s because I think I’m a better talker than I am. Maybe it’s because I’m a worse writer than I would like to be, but I think I could talk the theory out better than I can write it right now.
I wanted to talk out—not read my rough draft fully, but I have my notes and I’m just going to talk out the rough draft—and put it into the podcast format. This will be my rough draft of the theory of the markets. I’ll put it out, and if you’ve got ideas, if you’re listening and it spurs a thought, or I say something and you’re like, “I firmly disagree with that,” or, “I firmly agree with this,” or, “It should go further,” you can reach out to me.
You can tell me, “Hey, Andrew, here’s how to evolve your theory.” Then I’ll put this podcast out and hopefully it cures my writer’s block. My conversations with people—10 years ago, I used to think sitting in a room just reading was the way to invest. Increasingly, I think you need to read and you need to be differentiated, but the conversations I have on the podcast and with friends really help me think and evolve.
I think putting this out there and having a conversation with a few of you who respond with thoughts can help me evolve and perfect this theory I’m working on. That is my overarching reason and thought process for this podcast.
Let’s dive into it. Let me start with my theory of weird markets. Again, I alluded to this a little bit in the December random ramblings, but here is the theory as I have come to think about it.
The stock market is the most competitive game in the world. In all games, and across all competitive things, as the stakes get higher and higher and things get more and more specialized, the winning strategies at the highest levels often look counterintuitive or insane compared with what the game looked like when it was less evolved or what less evolved players would do.
Those insane and counterintuitive strategies do come to dominate traditional strategies as the game gets better and more competitive, and as the players get more skilled. I think the stock market has increasingly reached the point where the traditional strategies are just being dominated.
There’s the rise of all the money in pod shops. If you were saying, “Hey, I’m trading on credit-card data or something,” I’d say, “I think the pod shops can probably trade these quarters a lot better than you.” If there’s the rise of quantitative models, and you’re saying, “Hey, I’m trading stuff on value multiples exclusively. I buy things that are deep value, 5 times earnings,” I’d say, “I think the quant models can probably do that better than you and faster than you can.”
There’s increasingly machine learning and AI. If you were saying, “Hey, I’m doing some general trend following or rules,” I’d say, “I think the machine learning is kind of coming out there.”
I think the stock market is the most competitive game in the world, and increasingly, I think the traditional strategies are being dominated. What does that mean for you and me? I think the winning strategies for smaller investors who aren’t running pod-shop money and who aren’t running with $100 million computer systems are actually the only strategies—the only way to find alpha is going to be in what I am calling weird.
So, that is my theory of weird markets. If you want to outperform, if you want to generate alpha, if you want to be different, you have to do something weird going forward. You have to be investing in the weird. I’ve described them in the past as “N of 1s,” things that have no parallel in the market.
My favorite historical example would be Twitter and Elon Musk. Elon Musk, the world’s richest man—not a corporation, the world’s richest man—decides to buy Twitter on a whim. Then he decides to back out of it and claims an MAE. There have only been about 5 publicly traded MAE cases in history, so right away, you have something that hasn’t happened a lot.
The MAE cases in the past have been the global financial crisis causing banks to fail, or the individual company really getting hit—like it gets hit by a meteor. In this case, it was the world’s richest man wanting to back out, having cold feet, and thinking he was overpaying. It was an extremely strange case, and there were all sorts of things involved, but I would point to that as a really great example of an N of 1. That is my overall theory.
Let me go through some of the things I’ve been thinking about as I’ve evolved it. Let me start with my first contention: that the stock market is the world’s most competitive game.
Before I start talking about why I believe the stock market is the most competitive game, let me back up. I said that, as I talked about the most competitive game, games get more competitive over time. It’s really tough to evaluate that. In basketball, people will always debate whether Michael Jordan or LeBron James was the GOAT, and you’re comparing across eras.
It's really difficult to compare things across eras. But there are a lot of sports where there are quantitative standards that we can see across eras, and that makes it very easy to compare. Take track and field: if you run 100 meters in 10 seconds today, we can compare that to how the greats of 10 or 20 years ago performed. We can say, “Hey, the people today are faster than the people 10 or 20 years ago.”
Now, there would also be some debate that sports performance, medicine, athletics—all of this—is much better today. Equipment is much better, so maybe you have that. But you can say that runners generally are faster. One example I came up with is baseball. In baseball, in 2007, only 11 pitchers had an average fastball velocity of 95 miles per hour. In 2025, 300 pitchers had an average fastball velocity of 95 miles per hour.
Twenty years ago, if you were throwing 95 miles per hour or more with your fastball, you were an elite speed pitcher. Maybe you didn't have control or whatever, but you were literally one of the top 10 or 11. Today, if you're throwing a 95-mph fastball, there are literally 300 other pitchers like you. The fastball speed went from elite to commonplace in just under 20 years. That's 2007 to 2025.
I've got lots of other examples in sports, but let me give you my favorite example for thinking about the stock market and for comparison, because it's both so out there and because it's a combination of mental and physical: the Rubik's Cube. In 1982, the world's first—and, for 20 years, only—Rubik's Cube championship was held in Hungary. The winning time was 23 seconds. They didn't hold another world championship for 20 years.
The next world championship event for Rubik's Cube was held in 2003. The winning time was 20 seconds. That's a jump from 23 to 20 seconds, which isn't bad. At the elite sprinting level, a tenth of a second is how you separate the greats from the also-rans.
In 2024, there was that famous photo, if you remember, of the Olympic gold medal race where everyone's literally crossing the finish line at the same time. Noah Lyles, the American, won Olympic gold for the 100-meter dash in 9.79 seconds. The fourth-place time was 9.82 seconds. In that case, three-hundredths of a second separated “I am the world's fastest man” from “I am not. I have as many Olympic medals as Andrew Walker does.”
In the Rubik's Cube, I just told you that over 20 years, the time went from 23 to 20 seconds—three full seconds. That's a lot of improvement, but it's nothing compared to what's to come. The world championship took place basically every year from 2003 onward, with a world championship to organize and push people and push the sport, so to speak, to its limit. By 2023, the winning time for Rubik's Cube solving was approaching 5 seconds.
From 1982 to 2003, you go from 23 to 20 seconds. From 2003 to 2023, so the same 20 years, you go from 20 seconds to 5 seconds. That's just an insane amount of progression. But my favorite way to say this is that until 2019, the Rubik's Cube Championship actually had all sorts of events. There was solving it blindfolded, solving a 3×3—that's the classic cube puzzle—or solving a 4×4 or 5×5.
Until 2019, they had a category for the world's fastest person who could solve a Rubik's Cube with their feet. How fast could they solve it? In 2019, the last year they had the feet category, the fastest person solved a Rubik's Cube in 17 seconds. From 2003 to 2019, the sport improved so much that the fastest person in the world was solving a Rubik's Cube with their feet faster than the fastest person in 2003 was solving it with their hands. That's just crazy.
The other reason I like the Rubik's Cube is because the stakes are really small, right? It is Rubik's Cube. It is not exactly getting the girls. In football, the high school quarterback gets all the girls. “I'm the fastest Rubik's Cube solver at my high school” isn't exactly going to get the girls to come after you. And it's not exactly a monetary task.
The total prize pool for the Rubik's Cube Championship is $36,500. That's the total prize pool. The fastest solver of the 3×3—the best, most competitive event in Rubik's Cube—gets $5,000 for solving it. We're literally talking about a week, two weeks, or a month's take-home pay for an average person. That incentive is enough to push humans, within 20 years, to solve a Rubik's Cube faster with their feet than they were solving it with their hands 20 years before.
If that little pride and that little incentive can push people that far with Rubik's Cubes, what do you think about the stock market, where the rewards for being right—having one unique insight—can literally turn you into the world's richest man, right? One of the richest people in the world. If you can be right more than once, Buffett's net worth is $150 billion, and that's after giving away a heck of a lot of money. That's four times the GDP of a lot of small European countries.
If $36,500 in total is enough to drive this, what do you think about the returns in finance, where you can become the richest man in the world or one of the most respected men in the world? You're great at finance, and you can become the Secretary of the Treasury or the Secretary of Commerce. You might not even have to be great at finance to become the Secretary of the Treasury, by the way, but you can raise enough money.
I would just contend that if you can do that with the Rubik's Cube, step back and look at the history of finance. In finance, you see firms investing hundreds of millions of dollars to compete for milliseconds or microseconds of speed to win in trading games. You have the Rothschilds; the rumor is that they traded on Waterloo faster than their peers thanks to a racing-pigeon network.
Reuters, the news service, is named after a man who got his news service going by using a pigeon network to bridge a gap in the telegraph network. There was a gap, and he used a pigeon network so that traders could get news faster by pigeon than they were getting it by train. If you think about that history of hundreds of years of competition, where every edge is sought out and taken, and the insane riches that can accrue, I would just argue that if you say, “I don't think finance is the most competitive game in the world,” you're ignoring the history.
You're ignoring the stakes, the incentives, and the competition. So that's my point on competition. Let me go to the second thing I mentioned: as the competitive stakes get higher and games get more and more advanced, the winning strategy looks counterintuitive or even insane.
The first place I would point this out is basketball. If you and I are playing a game of basketball, our strategy is going to be much different from that of an NBA player. Why? Because NBA players can dunk and we can't. NBA strategy needs to revolve around, “Hey, the tall man who's running super fast—if he gets close enough to the rim, he'll just jump up, grab the ball, and throw it through the hoop.”
Whereas if you and I are playing, we don't have to worry about that kind of verticality. We will play on a horizontal level; they will play on a vertical level. That's a really nice example of how, when you're at the peak of your powers, the dimensions of the game can change.
As things get more advanced, the strategy can start to look insane. Let me give you an example. Everyone knows the Olympic high jump. It's been an event at every Olympics since the first Olympics. It was one of the first events in which women could compete. This is an event with a long, long history.
The high jump is where you literally set a bar, and you have to jump over it. Until the 1960s, the way everyone jumped over it was kind of the way most people would: you would run up and try to jump normally. Then Dick Fosbury won the gold medal at the 1968 Olympics with what became known as the Fosbury Flop. He jumped backward over the bar.
If you've seen high jumping, I'm sure everybody's seen it once, you'll know the technique. They run up to the bar, turn their body toward it, lean over, and their shoulders and head go over first, with their legs going over last. Dick Fosbury won with it. That was, and is, the best way to jump.
Before the Fosbury Flop came around, if you had gone to the best Olympic jumper in the 1950s and said, “Hey, why don't you jump over that bar backward?” people would have thought that was insane. But this is a better technique. I'm not saying that's the same in every sport. Obviously, in running, the best way to run has kind of evolved into us.
It's just interesting. Here's an event where, as people got better and perfected the strategy, they discovered a way of moving that seemed insane but was actually the optimal way to do things. If you tried anything else, you would get crushed today. Again, that is a physical event, but there are lots of examples of strategies in sports that have similar characteristics.
Let me give you one: in football, going for it on fourth down.
In the ’80s and ’90s, teams almost never went for it on fourth down unless they had to. Today, teams go for it on fourth down like crazy. Watch a football game: fourth and 4 at the 40, teams are going for that every time. That’s because of analytics, and people have realized that it’s optimal, and there are lots of reasons for that. If you don’t get it, you give the other team a short field. The reward for getting it versus kicking a 50-plus-yard field goal—the expected-points value—is much higher. All these types of things.
But if you imagine 30 or 40 years ago, if you had fourth and goal, let’s say from the 3, and you went for it, is that the analytically correct play? Absolutely. But everyone else was not going for it. If you went for it and missed it, you were probably going to get fired the next day, right? It was just so crazy.
Baseball—Moneyball has this famously, right? Hiring a first baseman who can’t really hit but who takes lots of walks showed that on-base percentage is much more important than batting average. Across sports, you’ll see lots of this type of stuff. The optimal strategy might not be intuitive. It seems insane at the time, but as the game evolves, people hone in on the optimal strategy, even if it’s crazy.
You can see that as AI has started to dominate a lot of places. Chess, as AI has mastered chess, I’ve got this great quote from someone who says, “AI mastered chess, and it did so not by playing like a grandmaster or a pre-existing program. It conceived and executed moves that humans and human-trained machines found counterintuitive, sometimes even simply wrong. The games that AI chess plays look like chess from another dimension.” Again, AI is smashing everyone else. It’s so much better than everyone else, but it’s making moves that, at the high end, people never thought of. They’re counterintuitive. They look wrong.
Poker—AI has started to solve poker. One of the things that AI has changed in poker is that, if you’ve ever played high-limit poker, you know most of the time the bet is in relation to the pot. So, if there’s $100 in the pot, people would think, “Do I bet 50% of the pot? Do I bet 1% of the pot? Do I bet half the pot? Do I bet the full pot? Do I bet 2× the pot?” AI started to evolve and discovered that it overbets the pot way more than normal humans do. You’d have $100 in the pot, and it would put a $500 bet in or something.
One friend who is a former semipro at poker told me, “Hey, if you looked at the AI models and you didn’t know that the AI models had evolved to win at chess, and you saw one of these play, you would think it was playing like a drunk uncle who had just come in and was throwing chips on the table. Every time it put $50 into a $10 pot, you’d be like, ‘This guy’s crazy.’” But the AI has evolved, and that aggressiveness has dominated.
I point that out because, in finance, I think these things are evolving. They’re coming to dominate humans, and I think the ways that they’re starting to win are going to look strange to a lot of humans. So, that’s talking about the high end and the meta level. If the meta level is evolving to a place where the competition is more fierce than ever, and where the basics are getting dominated by all this AI, machine learning, everything, how does an individual investor—a one-man shop, someone running a small fund, someone who doesn’t have access to or isn’t using this AI, this machine learning, these quant funds—compete?
I would argue that the way you need to compete is getting weird. The place that I would point to is that I don’t think you compete by saying, “Hey, I’m tracking credit-card data. I’m going to analyze this credit-card data better.” For a thousand reasons, pod shops have more analytical capability. Pod shops run with pod-shop quants. They can run with more leverage.
If the returns to getting a quarter right or modeling credit-card data right are 2%, that’s not worth your time if you’re not applying the same leverage. They can apply a lot of leverage and turn that 2% into 8%. Obviously, that carries risk and reward, but they’re better. They do it faster, they do it more systematically, and they just pressure the returns out of it.
So, what is the winning strategy? I think you can see the winning strategy at the edges of a lot of these games that AI has played. There’s a famous example—what is it?—Deep Blue versus Garry Kasparov in the ’80s or ’90s, when IBM created Deep Blue. Kasparov started his opening moves with moves that would literally be insane because he took the AI out of the book, right? If you have a chess opening move that no one would make because it’s so bad, and you make it, yes, that’s suboptimal, but you’ve taken it out of the book. The AI is experiencing weirdness, and it doesn’t know where to go, right?
You’ll see this in StarCraft, for example. They introduced AIs that could beat even the top pros, but only under very specific, controlled scenarios: one map with a lot of different things the AI was in control of. If you changed any of those scenarios, the AI could not evolve. As a commentator noted, the AI was the best player in the world unless there was even a minor surprise. Once there was a minor surprise, the AI couldn’t compete.
I think that’s how this applies to stock markets going forward. That’s my theory of weird markets. If you’re investing on fundamentals, you can’t do it simply on fundamentals. The AI competition is too great. You need to have the surprises. You need to have the weirdness.
The good news is that the world is a weird place filled with fat tails. It’s the fat tails that are going to present the opportunities for alpha. It’s where you can find a place to apply fundamental thinking in something that’s out there on the far tails.
I’ll give one example, and again, this is where I start to hit the writer’s block, but I’ll give one example. Speaking of AI, AI is a data hog and a power pig, right? If, 4 years ago, you could realize that the demand for power was one of the constraints on AI, you could make a fortune. I would argue that this was a place where AI models in a lot of places did not have the capability to do this. That was a fat tail.
A lot of places did not have the capability to see it, and it’s not in the historical data, right? You couldn’t look at a power play and say, “Oh, this is trading really cheap. Let’s buy it,” because it was the future. You couldn’t see the demand in the past. A lot of times, you couldn’t even see the demand in the future. You just had to know, “Hey, all this AI is coming. It’s not modeled anywhere, but it’s going to consume a ton of power.” So, I think that’s one interesting example.
I’ve said n-of-1s and weird situations. I’ll give some others. I think spin-offs are always a great place, right? You’ve got a company that’s spinning off a division. Oftentimes, the people who own the core company don’t care about or want the spin-off company. You get a lot of forced selling, you don’t have a lot of historical data on it, so you can do a lot of work on that. Oftentimes, the management team is a new management team, so you can get views that AI is never going to have. Pod shops might have them, but pod shops also might have liquidity constraints and all this sort of stuff.
So, I think that’s another interesting example. Unique event situations in the stock market—there are a few that I’m currently involved in that I won’t mention now. I do write up a lot of these, obviously, on the premium side. That’s kind of the bread and butter.
I think unique events are absolute catnip for this. The fundamentals might—here’s a great one: I’m long Warner Bros. Discovery, WBD. I’ve written it up, so there’s my disclosure. There have only been a handful of bidding wars in history, and at WBD, you’ve got a lot of soft considerations. You’ve got the Ellisons going around saying, “Hey, this was not our best and final bid.” You’ve got all this other stuff.
But bidding wars are very much on the edge of the markets. They’re very rare, and when they happen, I think each and every one is unique. You’ve got the Ellisons, some of the richest people in the world, personally guaranteeing an equity deal. You’ve got Netflix on the other side. You’ve got the Trump administration. You’ve got the Warner Bros. board. You’ve got all these different things. I think that would qualify as a unique one.
I’m trying to think of others off the top of my head. Again, this is where I like the first two-thirds of the theory, but then, translated into the specific market thoughts, I think as I talk through this, this is where I start to have the writer’s block that I mentioned and all that sort of stuff.
So, where else? I think there’s still a lot of alpha in management incentives. This is the classic non-GAAP spring-loading stuff, but when a management team—when you see the 8-K file that says, “Hey, this manager has decided to take all the next 5 years of their equity compensation this year as a reward”—I think those are very interesting situations. But I do think those are increasingly picked over, because if you say, “Hey, every time that happens, that’s an opportunity,” then AI is eventually going to learn that and everything. So, that’s one where I think there’s both opportunity and risk.
That’s it. So, look, I’m running long. I’ve done this on a Friday, and I’ve been thinking about this all week. I’ve got to go pick my daughter up in a second, so I’m going to have to go pick her up. But I think I’ve done—I hope I’ve done—a nice job of explaining the weird markets theory.
I feel like the first two-thirds of it—the overview, my thinking, my parallels to the sports world—I think those are very good. But I do have trouble, once I get to the specific markets piece, really driving it home with specific examples. Maybe I just need to think of them more, or maybe that’s a sign that theories can evolve. You can come up with a lot of interesting theories in your head, but sometimes it’s really hard once you put them to paper. The reason it’s hard to put them down is that the evidence doesn’t back them up.
So, I’m still thinking about it a lot. I think I’m going to wrap it up here. I appreciate you listening to me, but what I would really appreciate is if this conversation—me throwing all this out at you—spurs a thought for you. I’d love to chat with you. If you’ve got other examples, or if you say, “Hey, Andrew, here’s something you’re not thinking about on the weird markets side,” I’d love to talk. I’d love to do it, because I do want to get this theory posted in the next week or so.
I’m recording this Friday, January 9th. I want to get it posted because, as I said, all of my annual stuff is waiting on getting this theory out so I can link to it, mention it, and build off it. So, I want to get it out then. I’m going to wrap it up here and put this up. We’ll get the follow-up post, but if any of this is spurring a thought for you, I’d love to discuss it with you.
This is a topic I’ve been thinking a lot about, and hopefully it did spur something with you. And look, if it didn’t, hopefully you at least enjoyed me rambling and the Rubik’s Cube example. I mean, come on, how much better can you get than that Rubik’s Cube example? So, I’ll wrap it up here. Thank you so much for listening. We will talk soon.