He Quantified 200 Years Of Disruption Kai Wu On Separating Software Survivors From Value Traps

TITLE: He Quantified 200 Years of Disruption | Kai Wu on Separating Software Survivors from Value Traps CHANNEL: Excess Returns DATE: 2026-06-02 ---TRANSCRIPT--- Software stocks at least on this basis are trading currently at a 10% discount to the market which has never happened before over the course of this sample. What you find is that when you apply the value factor in the insulated sectors actually the poor performance has been great been been just fine. You almost see no difference between 2010 on and the beginning period. These companies survived and then ultimately thrived um despite you know being in the crosshairs of disruption. How did they do it? Two things. I think for many of these companies say software stocks I think the takeaway is that look the the code is not the moat right like for many of these companies code is one of the many things they do but you know we as investors need to look beyond that to ask the question of what other intangible assets or just moes in general do these possess.

Hey Kai welcome back. It’s good to be back guys. Our audience is uh familiar with you. Uh you’ve been on the podcast a few times. We always like having you back because in addition to running Sparkline Capital and and and managing the ETFs that you run and that you’ve built on sort of this intangible value framework, you’re also consistently putting out very interesting deep pieces of research on where the markets may be misunderstanding disruption, innovation, and the way that you look at sort of intangible value. and your recent piece that you put out in May titled AI disruption modes and value traps is looking at the recent sell-off in software and this possible you know opportunity that it has created and there’s this idea right now in the market that AI is going to be this existential threat to these software names and not necessarily an opportunity but I think as we work through this great piece that you did you know, we’ll kind of get into what the setup might be in some of these software names and sort of how, you know, you’re using your unique aspects of natural language processing and research to sort of uncover these possible opportunities. And so, this is one of the episodes where we’re going to be pulling in a lot of charts. Kai is going to be working through these with us. uh he shared these charts with us and our audience so we can get like really down to the nitty-gritty detail on the research that he’s done. So I just thought we’d start Kai with you know your exhibit two which kind of shows how the software where the software premium or lack of premium is today um and how unique that is in terms of you know software stocks after this selloff. So I guess the first thing just to set the context is that historically software stocks have commanded a premium valuation right historically investors have liked software stocks more than say the average industrial and the S&P 500 because they’re asset light because they have predictable SAS style revenues um and for a variety of other reasons that you fast growing and such. So over the past roughly 20 years um since this data began um they their forward PE ratio of software relative to the S&P has been at a 32% premium right so that’s been the historical average and there’s been some some fluctuations so it kind of dipped a little bit in ‘09 and then went on kind of a secular bull run peaking in 2021 if you remember that was kind of the COVID bubble where people were working from home and interest rates were at all-time lows and stimulus was coming to the market so software stocks were kind of at their all-time high valuations and then 2022 things started to reverse, valuations started to fall. Um they went through their historical average around 23 and then the past two years they’ve been continually falling kind of reverting back to um first parody with the market and then more recently over the course of this year have actually fallen to a discount to the market. So software stocks at least on this basis are trading currently at a 10% discount to the market which has never happened before over the course of this sample. Um there’s also a chart floating around from I think it was an Oakmarks letter. they’re another value manager but um they cited empirical research partners that take the same data back to about 1980 and and what they show is the same that over the past five decades um you know first we are at you know all-time lows with regards to the um spread between PE ratios of software versus the market and by the way that we’re at a a discount um in absolute terms um to say the the median stock or the average stock in the S&P um which is something that we haven’t really seen before And so that’s the one of the core ideas in this paper is trying to determine now that this selloff has happened and these are trading at these types of you know historically low valuations whether or not this is a these are possible value traps and so talk to I think it would be helpful I mean most people I think know what a value trap is but just talk to what a value trap is and then what you kind of why I guess value traps are problematic in the sense that sometimes when these securities get down to such low valuations, you know, they look like they’re no-brainer buys, but that but but they’re actually a value trap and they don’t actually add value or they don’t ever appreciate from that point going forward because their model is effectively being disrupted. So talk to that. Yeah. So all a value trap is is a is a stock or a company that is you know basically on its way to oblivion but that for um a variety of reasons appear cheap on traditional or on face value and standard metrics. Um and so for example a stock that has a low PE ratio but only has a low E because low PE ratio because everyone knows that the E is going to zero would be a classic example. Um what I show here in the paper is the example of four iconic companies. Blockbuster, Borders, Radio Shack, and Mlache, which owns a bunch of newspapers. Um each of which were disrupted by Amazon, Netflix, um you know, Google. Um over over the past couple decades, and and these were at one time, right, large multi-billion dollar companies. Um but, you know, over time they kind of um became cautionary tales. And and what I show here in this exhibit is actually interesting because um I compare the stock price to the fundamentals. In this case, the revenue per share in in the red. And what you can see is that you know when this disruption first happened um investors quickly panicked and started to sell down the stocks, right? So as the internet became more and more pervasive, stocks like you know Radio Shack and Blockbuster and Borders started to the price started to fall. But importantly the actual fundamentals only fell with a long lag. So in the case of Blockbuster took many many years for the sales per share to fall. In the case of Borders and Radio Shack, they actually increased their sales per share for a period of time before it all kind of fell. The wheels fell off the wagon. Now you know profits also were were maybe you know deter deteriorating as well over this period and in the case of mlachi there was an acquisition um and a lot of debt taken on but the overall picture I think is is pretty clear which is that you know to the extent that you know prices in the stock market are forlooking and they kind of price into some extent disruption um you’re going to almost always end up in a situation where prices fall faster than fundamentals which are lagged can keep up and so you’re always going to end up with a a window of time when say the price to sales ratio of these companies is looking really attractive to a traditional value investor yet um again that’s just kind of a a trap. It’s it’s suck sucking you into um you bringing you on board a ship just as about as it’s about to to collapse and sink into the sea. Um so that’s what a value trap is and I think these examples you know pretty cleanly illustrate um you know what investors as they approach the software you know boom should be should be concerned about. Yeah. And a lot of listeners or viewers might not realize, but you know, I remember when Netflix first sort of came out with its like, you know, you could get the three DVDs in the mail. And it was like, how is this ever going to, you know, disrupt like a blockbuster, you couldn’t see it. But with all these examples, you know, it’s always hard to see early on when this disruption is, you know, possibly happening in front of your eyes. And so, you know, that was another thing that I thought was very interesting in the paper. your sort of methodology for a way to dis measure I guess this disruption disruption exposure and kind of what that tells us about maybe the current environment that we’re in. Yeah. So I think the key here was obviously those four examples are cherrypicked. They’re they’re helpful anecdotes, but the the the question I really wanted to answer was that if you were more systematic about doing this, would it turn out to be the case that these um four examples of the blockbusters and borders are actually representative of a systemic problem that traditional value misters might face. So in order to to build on that and kind of set that up obviously into a way to kind of in point in time in in real time quantify um when there is disruption and which companies are are exposed to said disruption. So the way I did this was in a two-step process. Um I built on a paper I wrote in 2022 called investing in innovation. And so what I did was I looked at this um data set um of like you of all the patents ever filed um with the US patent and trademark office. Really cool data set goes back to 1790. The first patent was signed by George Washington. Um and it had and basically you can use it to to see over the course of the past two centuries um you know the the rise and falls of new technologies like from the automobiles to electricity to the internet. Um and and so what I do is I say obviously at each point in time you know hundreds thousands you know tens of thousands of patents what you care about is trying to cluster them into you know like groupings of similar technologies. Um and then from there to see which whether is an increase in um in the technology because oftentimes you find out you find that there might be false starts where say a technology starts to gain provenence but then eventually fades right electric vehicles were famously a competitor for the internal combustion engine but then ultimately lost out um you know 100 years ago or so. Um and so you want to find trending technologies. You also want to find technologies that aren’t only just trending but also are pervasive. And what I mean by that is that they’re not just increasing a lot in one specific subdomain. say like in healthcare but they but they also are pervasive across industries right so AI is kind of the best example they call it like a general purpose technology meaning that it’s you know applies of course to software use cases but should in theory be able to automate and and um you know do a lot of the the human labor that um you know is is obviously um a factor of production for most economic activity right so I I want like pervasive things I I look specifically for increases in um in patent volume that are pervasive across industries ries and so then that allows me to define the um technologies themselves. And then the second step is to figure out what exposure um firms and industries have to that disruption. And what we do is we look at a handful bunch of different documents ranging from earnings called transcripts to patents themselves to um you know company filings, analyst commentary to figure out which companies are exposed to each technology. So for example like if e-commerce becomes you know a thing which companies are potentially exposed to that disruption and then what we do is we actually roll it up to the industry level because any single company can be noisy right the data themselves can have noise. So you aggregate to the industry level so that you can say within retailers as a whole um you know even though some may be kind may not be exposed and some might be exposed on average they have this this level of exposure right so we’re creating an industry level um you know exposure and then in this chart that you pulled out here um you know I basically highlight over the past you know few decades I think seven different major disruptive waves ranging from the advent of internet infrastructure to e-commerce social media and then AI right And as you and as you roll through, I kind of look at what periods were each of these things these disruptions most prevalent when the pressure was the highest on disrupees um which are some examples of of patent clusters that um you know could collectively form this theme and then which sectors at each point in time were most exposed. I think one takeaway here it’s not always just it’s sometimes technology it’s not always right retailers and you know newspapers entertainment being good examples of of sectors that were exposed to say digital media or e-commerce. um you know even though they may not have been kind of the progenitors of that technology. Just a process question here and then I’ll let Jack go. Is when you when these clusters and this has nothing to do with the paper, it’s more about the process. When these clusters are being built or formed, are are you telling the um are you telling it what to look for or will it automatically does it find it like through it’s the the natural language processing or does the system find it these clusters automatically or do you do you have to instruct it as to what to look for? I mean there are some hyperparameters like how many clusters like how sensitive the to thresholds but but put putting that aside it’s it’s fully automated so it’ll kind of go through and say hey we look look at all the patents and then figure out you know form the clusters and then figure out which clusters are trending and which are not and then separately in step two identify companies and then industries that are exposed to each sector. So, for example, like I have my code set, you know, we could show up in in 20 years from now, you know, dusted off or whatever, and it’ll have a totally new set of of um technologies and companies. This just another aside, but does this at all allow you to like rank these disruptive waves against each other? So, like everybody’s talking about like the AI is the biggest disruptive wave we’ve ever seen in history. Like, does this tell you anything about that at all? I mean, I think it’s a fair point. Um, if you measure it by pervasiveness, right? As I mentioned, you know, artificial intelligence is meant to be a general purpose technology. Not that these other things aren’t necessarily, but it can in theory affect all facets, especially once robotics and and such are in play of the economy. I think one thing I would add though is that, you know, they’re all like dependent on each other, right? This is the idea of like the stacking the stacking of innovation over time, right? Like we wouldn’t have AI if we didn’t have um you know, electricity, right? Um we wouldn’t have electricity if we didn’t have I don’t know fire, right? Right. So all the technology o over civilization’s history has kind of compounded over time by building on each other. Um you know and and so I think it’s important to remember that like AI is obviously really cool technology. It requires really advanced computing. It requires you know big data which is obtained in many cases through you know the the advent of the internet being able to you know digitize information and and and put it into a uh a format that we can all see. So these things all tend to build on each other. And so you know maybe we are at the apex currently in terms of like where innovation and disruption is but you know a lot of that just depends on previous innovations that had they not occurred we wouldn’t you know be able to be where we are now. The the stack can get to this next chart we want to look at and poor retail is all I can think about. It’s been like getting the crap beat out of it by like every every innovation for decades here. But can you just talk about the idea of what we’re seeing here? Yeah. So this is divid 7. I take the example of retail. I mean it could have picked on a different industry. I guess it was mean. Um but the idea was you know to show through time it’s the the amount of disruption it’s being um u it’s absorbing from the seven or so different like um themes that I mentioned through time. So kind of the key insight here is that they stack. So in other words first e-commerce you know comes around the internet comes around and that you know obviously Amazon’s there and you know if you’re I guess Blockbuster that that that you don’t even make it past that. But let’s imagine that you do make it past that. Well, then next you have to deal with digital media and then you have to deal with social media and then you have to deal with AI, right? Like agentic shopping and such moving forward, right? And and these things stack. In other words, it’s not that companies today don’t also have to deal with so retailers today don’t also have to deal with e-commerce as a threat. They also just have to deal with that plus they have to deal with digital media plus AI. And so if you sum up the exposures to the to the all the different um themes over time, what you find is that you know, yes, they come in waves, right? the the peak of e-commerce disruption, you know, happened and then it kind of like settle subsided before social media really kind of came into play. But the trend is kind of this secular increase over time as, you know, company as innovation is accelerating as as technology compounds on itself on on each other, we end up in a situation where yeah, the the these companies are now being kind of exposed on all fronts. They’re waging like a multiffront war so to speak um you know against um you know innovations and technology coming from all different angles. Now, it also explains a lot because as a value investor, like if you’ve watched your value screens over the past ever decades, like these retailers have like perpetually been in them like they don’t ever leave them. Like these retailers in the mall and stuff like that, like those are they’re always sitting in these screens and so getting stacked. Yeah. Yeah. Long and Fitch over there. Yeah. Like I mean you’d be surprised like Justin, we’ve been running quels forever. Like you and I have seen these these various names like you would see if you walk to the ball right now like William Soma Abberro and Fitch. You got Claire’s used to be in there. Oh yeah, they’re all they’re all in there. Hot Topic like I don’t even know that even exists anymore. Like all these things but anyway back to the paper. Um this next chart gets this idea of the death of value investing. So before we get into kind of what’s going on now, it’s just good to maybe take a cumulative look at this and and how the value factor has performed and why it hasn’t been working for a long time. So can you talk to this chart? Yeah. So I mean, you know, I’m sure your listeners are somewhat familiar at least with this this idea. Um, you know, value investing, you know, buying uh, you know, beating down retailers, like the general idea has, you know, has been long, you know, a a long-h held tradition amongst many investors ever since Ben Graham in the 1930s, Warren Buffett, of course, being, you know, a famous proponent of the school. The thing is that value investing, you know, however we define it, has has really had a tough time of it. Um and and a lot of it has to do with disruption which we’ll get into more over the past couple of decades. You know obviously there are many different ways of quantifying it. What I’ve done here for this exhibit is to do something pretty simple where I create a long short factor. So in other words you long the cheap stocks short the expensive stocks on a valuation metric. In this case it’s a blend of I think four different things. Price to earnings ratio price to book ratio price to sales ratio and um price to free cash flows. Um and basically the reason why you kind of diversify across you know different metrics and and the point is this which is this is the factor that and if you extended the back test all the way back to um the initial work 100 years ago you would have seen consistent outperformance for decades and then around um 2010 you would have started to see a draw down. it starts to turn over and really has never recovered. Um you even even as of today, right? And so this is what has led many people to declare this the death of the value investing that you know perhaps we should be doing meme stocks or or um you know the principles of value don’t apply anymore. Um and you know value investors have lost plot. They’re all too old school and they’re just buying you know a bunch of uh abberroy stocks or whatever right. Um and and but you know to me it’s always been there’s that can’t be right. I mean value investing makes sense by definition. it’s just maybe the way we measure it that could be problematic. Um and so this is kind of a really interesting study that we did here. Um and and kind of the conclusion is that you know value investing is is not dead. Um it’s maybe just being disrupted. So what what I do here in exhibit nine and yeah this is really the key exhibit of the of the paper is I say let’s not apply the value factor to the entire stock market but let’s instead apply it separately to two different parts of the market. Um first would be what we call exposed industries which are the industries for which um their technological exposure score which we showed in the case of retailers exceeds a fixed threshold. Um and then that would be one and then the second would be insulated industries. So those industries that are not exposed right and those two things collectively by definition incre. So divide the market into exposed versus insulated halves. Um what you find is that when you apply the value factor in the insulated sectors actually the poor performance has been great been been just fine. You almost see no difference between 2010 on and the beginning period. In other words, value has worked just fine as long as it’s not in industries that are exposed to technological disruption. However, if you look at exposed industries, industries like you know retail starting in you know the mid um 2010 or around then or a little earlier you find that um the performance has been quite bad and so starting in 2010 you have this big draw down and by the way the draw down so big it overwhelms the positive returns from applying the factor in the insulated industry such that the net return for the factor is negative. So said differently, you can explain you the demise of value investing um you know through the lens of you know if you want to apply these traditional metrics to you know re um real estate companies or you know asset heavy businesses fine go ahead and do that. It it’s no different. But if you want to try to apply it apply it into sectors that are now exposed to technological innovation um not just software but but company but sectors like retail that you know maybe initially were not exposed but now are heavily exposed. You’re going to have some some issues and that’s not going to work. And then to the extent that the market is more and more, as we’ll see, inexposed industries, that’s going to overwhelm um the positive returns you get um from this kind of vanishingly small um part of the market that is insulated. It’s such an interesting point because if you go back to my point about retailers with the mall, like if I had known in advance that they were undergoing a disruption, I could have not applied value investing in that industry and and that’s how it proved out to be the truth. like you did not want to use value investing in in retail like basically at any period in the past you know however many decades plus you know right and I think it requires two things one is a recognition that hey this disruption is coming and second a recognition kind of like a lack of hubris to be like yeah you know and by the way I’m not going to try to apply my metrics in this thing because I just don’t think it’s going to work right Warren Buffett talks about this circle of competency um you know for the longest time he avoided tech stocks he said this is just not my cup of teet I don’t know how to I just don’t do this which works when tech isn’t like the entire market and then when it is you’re in cash. So this next one you did some robustness checks to to check deeper to make sure there’s nothing else going on that would explain what’s why value is not working here right. That’s right. Yeah. So um one one fun aside is that this paper is kind of the first one that I did where like I relied heavily on in this case cloud code um to do a lot of the experiments. And so what the fun thing was that you know I basically did the base I created the baseline script and once I had it I was like hey you know what like I want to test like a bunch of different things you know not just the US I want to test it in global stocks international stocks emerging market stocks I want to look at like you know sectors subindustries industry sector groups so on so forth right and so kind of the workflow ended up becoming like hey you know Claude can you like take this and like apply it to the other things like show me the results let me look at the table with you I have a couple follow-up questions so on so forth so it’s kind a fun way to scale like the the analysis um you know by kind of delegating a lot of the um you know robustness exercises to to Claude um but yeah so that allowed me to cover a lot of ground I mean what I’m showing here are are just six or I guess five five of the major robust checks what this shows by the way is the um the spread between exposed and insulated returns so remember like um this is a negative number because um when you apply the traditional value factor in exposed industries it does worse right than insulated industries And so the baseline shows a spread of negative seven percentage points per year. That’s very bad. Um and then you can say what what if you look at just global stocks, not just US stocks. You know, kind kind of not so good as well. What if instead of looking at this blend of valuation metrics, we focus just on the canonical FMA French price to book ratio. Okay, that doesn’t work. Um what if we sector neutralize? I guess it’s a little bit less bad because you’re explaining some of the variation. But even within sector, you’re seeing that the company, you know, that there is kind of a an effect here. What about if you do um so one popular thing these days is to do these double sorts where what investors will do is they’ll say I recognize that price to book or price earnings could have value trap risk so therefore I’m going to intersect it with um say ROE or some profitability metric um in order to or momentum um in order to say ideally weed out value traps. So you want companies that are both cheap and and profitable or cheap and have not bad momentum. Well, it turns out that, you know, that actually helps, by the way, an in absolute, but on a spread basis, it’s the same, right? So, you’re you’re not maybe the lines are are not going from positive to negative, they from from positive to positive, but less positive. But the point just being that the gap um you know, remains in this case 6.3 percentage points. So, like a meaningful meaningful gap. Um so look I mean the point just being that th this this finding seems to survive you know the exact specification of the sign value signal the universe applied to um and um and so on so forth. So it’s a you know pretty robust finding. Yeah, we just did an episode of Cliff as this and this reminds me of exactly what he’s done in a lot of his papers like his international paper or value investing is dead. He’ll ask like every question as to what could possibly explain this and then once he’s eliminated all those he’ll say all right my conclusion is okay. Well, I mean, I guess it’s impossible to prove anything, right? So, we had to just kind of narrow it down and try to throw out as many competing hypotheses as possible, right? So, so exhibit 12 is it kind of gets back to what I asked at the beginning, but this idea of how big the disruption is. Um, so what are we seeing here? I mean, obviously this is this is a very large number of companies that are exposed. Yeah. Yeah. So what we see is a number that the percentage of market cap in say I think this is in the US market um exposed to innovation um however defined increasing from about 40% to 70 mid70s 75% let’s call it over the past 20 years right and and that’s the result of two things so one is the fact that um technology is affecting more and more industries right if you went back to the 1980s tech was just like IBM right and now tech is like all companies have tech um to be to give you one kind of simplified example. Um and second is just that the tech industry or whatever you would call these technologically exposed industries to be more precise are just a bigger part of market cap right but the point is that even if you look at things on an equal basis and on a names basis not just market cap you get a similar result I think it’s 72 versus 78%. And by the way if you look outside the US the numbers are a little bit less extreme. So within like developed markets or emerging markets um the numbers aren’t quite 78%. But they are still above 50%. And they still have the same feature of an increasing trend. So even within emerging markets which have been of the you know been the kind of least technologically advanced of the major economies um you do find a a trend where you know the idea that hey I’m a value investor. I’m going to do the thing where I just like hide in nonexposed sectors. That kind of like has been uh you know increasingly challenging thing to do as those non-exposed sectors kind of go away. Yeah, this this reminds me. We talked to Andy Constson in the last episode. He was talking about this idea of what’s different from 99 to now. And one of the differences was like tech’s just a way bigger part of the economy and the market than it was then. Um, which I think kind of is sort of a correlary to what you’re talking about here, right? And not only is tech as a, you know, say gigs or MSI definition a bigger se bigger part, but tech cross cuts across all industries, even in industrial, is more reliant on technology today than it was 25 years ago. Um, so yeah, but I would agree with Andy’s point on that. So I guess the big question here is how do we differentiate the companies that are going to survive this that are going to thrive in this versus the companies that are going to be disrupted. And I think as we get further in the paper that that’s what we were trying to address, right? Yeah. So I think this is where we switch gears. So I think the first piece part of the the paper was more around you know what not to do, right? And now it’s like okay so like can we actually study history and can it be actually illustrative onto what what you should actually do, right? Right. And so here’s where I bring the examples of Walmart and New York Times into the discussion. You know, obviously Walmart is a retailer, New York Times is a newspaper, two of the most um beaten down industries um from disruption. Um what you find is that these companies survived and then ultimately thrived um despite you know being in the cross of disruption. How did they do it? Two things. First, they you know maybe not initially but eventually leaned into the uh technology that was disrupting them, right? Walmart you know has one of the biggest e-commerce businesses today. And second, they um you know leaned into their unique intangible assets um that you know outside of technology let’s say that allowed them to be who they were right their brand or human capital and network effects. Um and so you know this actually this is where I bring in like a paper I thought was really interesting by this guy named David Tease. So this paper um was about like who profits from technological innovation. Um it’s written in like 1986. Um but the principles while you know dated are are timeless I think and and you know the key insight was this which is that you know the the long-term winner of an innovation isn’t always going to be the one the initial winner or the innovator itself right oftentimes the person the firm who ultimately approves the value or captures the value of an innovative cycle a disruptive cycle is not again the the core innovator but in fact the the firm that possesses the complimentary assets this is his terminology complimentary assets that surround the innovation. So I have here an exhibit 14 that shows some examples of um you know this is from Durking his paper um of things that are considered complimentary assets that’s manufacturing, distribution, customer service and then complimentary technologies. So outside of the focal um uh IP or other IP that surround and kind of cement a motor around that, right? And he gives all these really cool examples. He gives the example of a company called EMI which is a UK based company. They actually invented the um the CAT scanner um which is a machine you know they sell to hospitals but the problem was that selling stuff to hospitals was really hard and it turns out that you need to like you know do a whole enterprise sales cycle and then you need to like do what effectively for deployed engineers like train their like the people how to use the machine and then service it once it breaks down like and that’s a really hard thing for them to do. Um what ended up happening was GE General Electric came in there and they had those other complimentary assets in place. They didn’t have the technology itself, but over time they figured it out and they developed it and then they won the market. They have another example he talks about called like RC Cola which I guess was like a small cola company. They sell they actually invented diet the diet and canned cola. So that was an innovation at the time but they didn’t have like the shelf space or the distribution of their brand that Coca-Cola and Pepsi had and they obviously won that market. And then another example was kind of the opposite case is um IBM. Um T talks about how IBM at the time was late to the PC market um but they managed to capture it in the 80s at least um and he says it’s not through their the strength of their technology but rather through um the uh the ecosystem of software and peripheral that they kind of built around the IBM like um you know framework right what we would call network effects today um and so you have these these examples which are quite illustrative right so who won you know who wins IBM Coca-Cola and GE right they win on the back of these intangible assets even though they weren’t the one who actually innovated the underlying technology. They they didn’t invent diet coke, they didn’t invent the cat scanner, but they had the necessary assets to win the market, right? Right. So I think that’s a really important lesson to think about as we approach like the software selloff that yeah these these these software companies they’re you know if their core mo is code then yeah maybe they are in trouble but if it’s the complimentary assets around that then you know prot’s framework they actually might be fine right and conversely yeah anthropic and open are the early winners they they are the innovators um and Google I guess of this new technology the LM but that doesn’t necessarily mean that they will capture all the profits um because there’s so many other things that matter when it comes to the way that these competition dishes unfold. Well, first of all, RC was really good. I don’t know if you guys ever had it, but it was actually I thought I I thought it was better than Coke and Pepsi. Um, really? Do you ever have Justin? Okay, Jack. No, but you are a soda expert, so I would imagine your ranking would be important here. I mean, you don’t you don’t run the 530 miles Justin runs if if you’re drinking things like RC Cola and it’s people like me, they’re they’re doing more of the consumption of RC Cola. Um, but the other thing that was interesting, Kai, is we were talking about like you were talking about the idea of leaning into the disruption. And one of the things that crossed my mind is it’s interesting because like in the past you’ve had like the Walmarts who had to lean into technology disruption. Now this time you actually got tech firms that have to lean into technology disrupting their technology which which makes it a little bit unique what we’re going through right now. Yeah. I mean in a way they are I guess positioned a little better than some of the the firms of yesterday year when the new tech technology comes around because they’re already kind of techf facing. Is it a different type of technology? Yes. Right. like being a good AI uh coder isn’t necessarily the same thing as being a good traditional engineer, but it’s definitely a lot closer than, you know, being somebody at, you know, at at Blockbuster, let’s say, when when um when when the streaming comes around. Um and we’ll see the data on this, too, that that software companies are, you know, amongst the most aggressive in terms of um their adoption and um investments in AI. So they they see it coming. They know it’s a threat and they know they’re vulnerable and they’re doing in many cases what they can in order to offset um that exposure. So one of the things you talked about in the paper which we’ve talked about in previous interviews is this idea of intangible modes. Um their ability to protect themselves using these things. So before we get into that, I thought maybe it would be good just to revisit that quickly. I’ll put up this exhibit 16 here quick. Intangible value. If you can just talk about the different intangible modes that you measure. Right. So I I touched on each of these. So the first of the four is intellectual property right this is not just patents but any kind of proprietary knowledge data you know software technology um second is brand equity that’s customer relationships um you know brand loyalty things like that third is human capital that’s not just the position of a talented workforce but also one that’s culturally aligned around a common goal and then finally network effects um which is this ecosystem of external producers and consumers we saw this with IBM you know examples today might include like Uber or like the New York Stock Exchange, Great Ice. Um, and so, you know, when you have these four types of intangibles, they can be, you know, really important. And in fact, I would argue that, you know, most value today, most value capture today, um, in the economy is is due to these four intangible pillars as opposed to traditional tangible capital, which I think is, you know, just having a lot of book value. Does that really a moat, right? Does that actually provide you give you the ability to kind of earn um, excess um, ROIC? I would argue probably probably not. Um but yeah to to your question though how does how do we build the metric? It’s we have like a bunch of different like underlying proxies for the for these pieces. So for example like you know we looked at um for the traditional value metric you know price to earnings combined with price to book price to sales in this case for IP we might say let’s look at you know the patents to price we might look at um R&D expenditures to price so on so forth smoosh them together into a metric do the same for brand with like trademarks and and um you know social media human capital with maybe job postings and and you know employee profiles and you create like these scores um again similar to what we did with a traditional value these yield based metrics looking a price relative to X, X being a measure of intangible capital for these four different pillars. And then we combine it into one final composite score so that every single company in your universe and you know whatever 5,000 global companies have a score that you can then kind of look at at least at any point in time who’s high, who’s low, and you can kind of build factors around it the same way we did with traditional value. And as we get into exhibit 17 here, this this looks at that idea that traditional value investing is not working in these exposed industries. But when you adjust and use intangible value investing, we get a different story, right? Yeah, that’s exactly what we see here. Right. So, if you remember the the exhibit from before, we saw that, you know, um the uh traditional value applied in the whole universe worked okay and then it stopped working in around 2010 and it was in a draw down and that was you know he split it into two pieces. It did totally well in insulated industries but struggled in exposed industries. what we’re seeing now for intangible value. So once you look at not just the traditional metrics but you also add in these intangible modes what TE would call these complimentary assets right what you’re finding is that um the factor works in insulated industries as it did before but most importantly it now goes from not working to actually working quite well in exposed industries right because exposed industries if you remember like put aside the jargon these are just industries where you you’re facing disruption from a technology whether it’s e-commerce whether it’s um you know cloud computing or social media or AI um and you know What allows you to survive, what allows you to be the New York Times or Walmart are these competing assets, these complimentary assets and in addition to of course being you know embracing the technology itself, right? All of which are in theory captured by this horror pillar framework. I think this is actually an important point right that you know so so even just step back like the TE’s framework and the intangible value framework are kind of like very actually related. Um so you think about it this way which is like T says there’s a focal innovation right the focal innovation is a subset of the IP pillar right so a company so of course when we go out and we say what is the intangible value looking for is looking for companies that are doing AI of course but we’re not just looking for companies that doing AI we’re looking for companies that also do other types of innovation other types of IP as teach would call he would call them um you know complimentary intellectual property innovations like in robotics right like in genomics and we go even one step further and say we’re also looking for companies that have strong brand modes, human capital, network effects, the the the true complimentary assets, right? So, we kind of want all all these different things. And so, going back to the the exhibit here, right? What what you find is that once you kind of look more holistically outside of just backward-looking earnings and and book value and look at what intangible most companies have, now you’re starting to be able to find put together a framework that now works not just in insulated, but also in exposed industries. Also, when you’re facing technological disruption, you’re able to um you know, be able to separate the kind of Walmarts from like the the Blockbusters. What’s interesting too is in exhibit 18, like if I was trying to put together a more ideal value strategy, what I would want it to do is is work regardless of the disruptive period. I wouldn’t want to like figure out if I’m in the disruptive period. I want to just work regardless. And I think that’s what you’re getting at here with intangible value versus traditional value. That even in this disruptive period, non-disruptive period, the performance has been pretty similar of intangible value, right? The key is consistency. Like it’s it’s I guess we call all all weather, right? It appears to work. So first of all, what this exhibit does is it cuts it into two dimensions. one is um in exposed versus insulated and the other dimension is um by time. So we’re looking at the first half of the sample when things were kind of better and then the second half when things have more challenging for traditional value, right? And so what you find is that intangible value regardless of the time period or whether you’re looking at exposed or non-exposed industries has tended to be pretty consistently, you know, around the same um L performance. Um whereas if you look at the traditional, it’s highly dependent. If you’re looking at like the first half of the sample and the insulated industries, you do great. But as soon as you start to go more recent or you start to go to more exposed industries, traditional volume kind of value kind of falls down, right? So that that’s the challenge which is like when it becomes so contextual then like yeah if you do factor timing it can work but like you have to be right then you need to have a good model and a good understanding of when to apply it and when not to versus it being moral. This next one’s really interesting because you actually looked back to 2007 and you looked at the companies were out there and you looked at traditional value and you looked at intangible value. You looked at what they agree on, what they disagree on, and then what ended up performing well. So what is the lesson from this? Yeah. So what this exhibit shows, it’s like a it’s a matrix, a two dimensional thing. So on the x- axis shows like the traditional value score from expensive to cheap. On the y- axis it shows the same but for intangible value, right? So we have like the four quadrants where like the diagonals are where they agree and then the off diagonals are where they disagree. So the upper right is where you know um companies where both metrics agree the lower right where they both disagree. The lower sorry lower left the lower right is where um you know a stock might look cheap and traditional but not intangible. And then in upper left it’s the it’s the opposite. So the other thing I did here is I color coded each each dot into three colors. So blue means it’s a company that over the next 10 years was a winner, right? Apple, Kroger. Um gray means it did okay and then red means it was a loser. like Las Vegas Sands, GameStop did not do well from 07 to 2017, right? Um, and what’s what’s immediately visible once you look at the colors is is first that intangible value worked pretty well because most of the blue dots, the winners were in the top half of the exhibit. In other words, intangible value regard regardless of whether where it scores on traditional value, cheap intangible value stocks had have done well the next 10 10 years. Um the other thing you see is that um in if you focus on the off diagonals is that traditional value had some had some challenges that like stocks that looked cheap on traditional value but expensive on intangible value like Macy’s or Wells Fargo tended to be losers and stocks that looked um expensive on tangible value but cheap on intangible value like Amazon or Apple tended to be winners, right? And so this kind of explains I think more intuitively what we just saw. Like why was it that traditional value struggled in exposed industries? Well, it was because they, you know, sold the Amazons and they bought the Macy’s. Um whereas, um, you know, intangible value because you’re now taking into account say the modes that the intangible modes that an Apple might have, the network effects, the brand, the human capital, the IP, suddenly Apple no longer seems expensive, seems cheap, right? So, it helps you kind of more discriminate between companies that might seem expensive optically but are actually truly disruptive and also companies that might seem cheap optically but actually truly being disrupted. What struck me the most about this is no blue dots in the bottom. So there were no like extremely inexpensive companies according to intangible value that ended up being the biggest winners, right? Not not in like the bottom like uh yeah. Yeah. The bottom of the whole chart which means that like there it was measuring value, right? Is I think what it means because there were no there was maybe there were some that were slightly expensive go forward into a tangible value but there were none that like extremely expensive according to tangible value that then ended up being like an Amazon type company, right? I mean to be clear this was just the top 100 stocks, 1000 larger stocks at the period in time. So there could have been like some other names that would have been there. It just would have been too many dots. So I didn’t want to show you know the thousand dots. But it still is pretty interesting. So this next exhibit gets at the idea of looking at the same four quadrants, but now we’re looking at return by quadrant. Right. Yeah. So all I wanted to do here was just make sure that the results generalized. The the previous chart showed just a 10-year period from 07 to

17. Now I wanted to look at the full sample. Um but the the setup’s the same. And so what you see is, you know, the stocks that both um metrics agreed were cheap did the best 4.2% annualized returns. Stocks that they both thought were expensive did the worst negative 5.1. When there was disagreement, um intangible value one. So in other words, the quote unquote expensive disruptors, the Apples and Amazons, um did well, 2.8. Um and then the value traps, the stocks like the Macy’s, right, that were that look cheap on traditional but not on intangible value did negative 1.6%. Now a couple interesting findings. So first of all first of all is the fact that yeah you know the the intangible modes do appear to matter. So that’s good. Um the second thing we find though is that you know the when they when there’s agreement it’s actually more powerful than when just one u when just intangible thinks something right and so that that kind of goes back to this idea that I think we’ve discussed on the podcast in in the past that potentially there’s a com a role for these two metrics to be complimentary with each other right that you know the real red flag is not when something’s just expensive and intangible value when it’s also expensive on int on tangible value when it’s expensive on both metrics that’s like pretty concerning right um so I think that that’s another, you know, interesting takeaway from this exhibit to me.

So, this this next exhibit, we’re we’re actually taking this now we’re applying it to software. So, we’re looking at a tangible value score and you’re putting some of the names here that a lot of people would recognize and looking if they at whether they’re cheap or expensive on a tangible value, right? So yeah this so what we found up to here we spent most of the paper talking about like the historical um you know uh disruptions like going through the past waves thinking about um you know what metrics do and do not work what what like we went through TE’s framework of complimentary assets to understand what you know how to think u about u about modes so now what we do is we kind of say let’s bring it all to the present let’s all put it together in a way that would be applicable to today we’re going through the current disruption with with software stocks having sold off significantly um due to um AI disruption fears. Um what this chart shows 21 is stock is software stocks that are um down 30% or more in the past one year. So these are not like your software stocks that are like this is not all software stocks but it’s the ones that are considered losers because of AI generally um over the past over the past year. And what I did was I showed the distribution histogram of the intangible value scores for these names um at this point in time. And so the first thing you see is that the average is positive, right? It looked to be about like three or something. Um suggesting that yeah these stocks which are in a large draw down they sold out like 30% with the market up 30% over the past you know uh seven or eight months. Um so a 60 percentage point spread um that these stocks may on average have been oversold. Um you know shoot first ask questions later. Um but the second thing you see is is pretty decent dispersion and and more importantly dispersion on the left side right? So look at the left tail on the red. This is actually really important because this is not usual, right? You don’t usually see this much dispersion on the left side of companies that are basically value traps. Companies that, you know, um have are down 80% or something but are still expensive on these metrics, right? That’s, you know, generally unusual thing to see. Um and again, don’t take don’t read too much into these logos. They’re chosen for illustrative purposes. Um but like, you know, you you do see that like, you know, look at like HubSpot versus Salesforce. Both of these are kind of CRM type companies. Salesforce is looking at least on these metrics, you know, on on the cheaper side whereas Hopspot looking more expensive. So you do see some dispersion even within um comparable names which I think is is worth with is worth noting. It is interesting by the way too cuz to your point like GoDaddy um you know registering domain names, building websites like I I saw Wix I think is laying off a bunch of people like it makes sense like where these are based on what you would think in terms of what their votes are. Like it would seem like a GoDaddy would not have a very strong vote, right? Right. And and so there’s another exhibit I have in this paper where I actually use this framework of the four intangible pillars and say like what are the you know what are the most that a company might have right so like accumulated business logic um like embeddedness in customer workflows customer relationships regulatory compliance burdens right and so one of the insights here is that and this is pretty intuitive I think most people know this is that you know more enterprise facing company uh um software companies that face like the largest enterprises will tend to actually have wider moes because this the switching costs are a lot higher. These things are a lot more embedded. There’s systems of record. um you know the the compliance um um requirements are so much more ownorous um than say consumerf facing things like you know GoDaddy for example um you know or I’m think Dualingo here too um where it’s a little easier for a random person just switch off an app right um and so I think that these things do correlate and you know you you can look through each of the four intangible pillars and and you know I have this in an exhibit actually and and look at you know kind of score you can sort you know a bullet point by bullet point to say hey which for a given company X where’s it score on on these you know four intangible pillars and then on on each of the say 20 or so sub points within those pillars. So there’s basically two ways I think if if I’m a software company there’s there’s two ways I can succeed here and and I think you get this in the paper. One is I can have a mo which we’ve talked about. The other is I could really embrace AI. So as we get into the rest of the paper those are kind of the two things you’re looking at right in terms of the way to differentiate these ones that might succeed from the ones that won’t. Yeah. So if you remember the last paper we did together was on on it was called like a AI adopters beneficiaries of the boom and the idea there was to find companies that are positioned for AI adoption right because presumably they over time if AI becomes a thing would have a um would separate from the lagards um and that was like a one-dimensional thing what I’m saying now is let’s take David Tiss’s framework and say hey look that’s obviously important but it’s not the only thing that matters right the fact that Walmart figured out e-commerce was was important but they also had a lot of other things going for them right that allowed them to to be to survive um relative to any other legacy company that was trying to become a e-commerce company um and and that is the complimentary modes right so I’m adding to the AI adoption lens this additional lens which is you know really the remaining parts of the intangible value for builder framework um so that you know together you have these two things that sum up to the intangible value framework but I decompose in an interesting way where I have AI adoption and then everything else um and you can kind of look at those things at almost distinct distinct um you lenses. One of the points that you brought up in the paper was, you know, some of the firms that actually survived this disruption, AI might actually help improve the margins and the profitability of those companies. Um, can you just explain the logic in your thinking there? Yeah, look, I mean, the idea is that obviously there’s a ton of dispersion, right? So, in the software sector, there’s some companies that are aggressively adopting AI, others that are doing not much, some that have defensible modes, others that do not. And so there’s going to be some winners, there’s going to be some losers. But when all when the whole shakeout happens and all is said and done, the companies that do survive are actually in an interesting position because you think about like what is the biggest cost center for these companies, right? It is the production of code. That’s like the you know main factor of production for for these companies at least from a cost standpoint. Um and and and bringing to this the the additional complexity around stockbased comp. So stockbased compensation has become this big big flash point amongst the investment community because these companies have always software companies have been really kind of liberal users of SPC for a long time but now that their stocks are down investors are kind of like wait a second what’s all this like why why are we doing this right because you know software engineering talent is expensive um and so to the extent that um AI has the potential to um you know reduce the labor intensity of software code that’s actually you know potentially going to alleviate this bottleneck allow these companies to you know do what they’re currently doing but at a fraction of the cost or said differently to for a fixed number of employees be a lot more productive, right? Um and so you know you you you could conceive of an argument or actually you know contingent on surviving which of course is a big if um you know AI is actually a boon um to to these companies. talk about this next chart the uh you mentioned the dispersion but this the sparkline AI adoption score and you know AI exposure and sort of this you know you’re seeing to your point like software you know companies are way up to the right so they’re obviously embracing AI but um yeah like how should we be kind of thinking about this would you would you say yeah so this this chart here this exhibit 26 is comparing like two different analyses I did over different points in So on the x-axis it shows exposure of a given sector to the technology of AI right so in other words to what extent can large language models in theory impact the day-to-day tasks of a company um so exposed exposed sectors of course software um banking um hardware pharma non-exposed sectors are like you know um I don’t know food and stables retailer or whatever right um you know and this again this is on the factor this is on the production side and then on the y- axis this we see the adoption score right and so what this here is is showing the um extent to which these companies are leaning into AI whether it’s they’re hiring hiring AI employees getting AI patents repositioning their businesses for AI and then what I did here was I showed all the different industries in a scatter plot and I draw a red line which is basically like the the the line of best fit the average and so any company any sector that’s above the red line um in theory is adopting AI more aggressively than they are exposed so they’re kind of your like early adopters and anyone below the line is actually kind of lagging like they relative to how exposed they are they’re really not doing enough. Um and so yeah, software is actually you know the outlier here in terms of being you know they have the highest um have the high highest exposure but they have by far the highest adoption right um you know so they are as I said earlier you know truly recognizing the extent of the threat and you know on average at least not everyone’s doing it but um but doing you know the best they can um to to respond to it right you I have another chart in my paper showing like AI job postings and you know software and and software services and IT services are like by far the highest sector when it comes to um you know the the hiring of AI talent. So this next one is sort of like the sweet spot where we’re coming back into software and now we’re looking at the software companies that are high or low based on AI adoption and higher low based on intangible value. Right? So all I’m doing here is putting these two dimensions together. So remember one dimension was how much AI adoption a company has and then the other adoption was the everything else section which is your intangible value score minus AI adoption so we’re not double counting um and what I do here is I show in this case this is for the software selloff so all the software stocks that have fallen 30% or more right um over the over the past year um so these are kind of like your your software losers or perceived to be losers um you know by you know based on the AI disruption and what you can see is the upper right is where you want to be. Upper right is a sweet spot. These are companies in the upper right that in theory have a strongly defensible business due to the strong brand, human capital, network effects and complimentary IP yet are also leaning into AI. So they kind of have the the full package. Um and then in the um you know lower left are the opposite. So companies that have you know very limited intangible modes um you know and are not doing enough in AI. And then there’s the kind of middle category too. And so you do see, you know, that there are a handful of companies in the red um and then you see um some companies in the middle and then the vast majority of names are in in in the kind of not so good section, right? So the point just being that there’s a ton of dispersion, right? That there that there are, you know, plenty of companies out there that are, you know, that have good pre-existing businesses, plenty of companies out there that have good um AI, a few number that have both um and many that um have have neither. And then you have the next chart is the high dispersion of disruption scare stocks. So there’s a lot going on with this in this one but explain to us what we’re sort of looking at here. Right. So I I’d already observed earlier that you know software stocks have huge dispersion right so if you go back to the very beginning of what I mentioned like software stocks are down 30% um as an index right the IGV done down about 30% um peak the trough but there are is huge dispersion right like you know GoDaddy Salesforce is down 50 to 80% um Adobe um you know some of these other names are are down big and so you know you see that and then you also see the thing I showed a few slides ago which was that um u the intangible value scores right are you know have this uh have a wide dispersion as well with like this this big left tail of copies that are potentially value traps as well. So the third element of dispersion I wanted to bring into the mix was this idea of you know historically when you have these events happen what happens over the next year to returns right because this is this is another way of measuring dispersion now obviously today it’s software stocks but if you go back through time it would have been newspapers it would have been retailers right that would have been the kind of exposed sectors so in order to build a metric of you know who are the kind of the the folks in the crossairs of disruption what I did was I said I said this I said let’s look for historically um companies that were in both exposed sectors. So remember the definition from before technologically exposed sectors that are also over a trillion 12 months in a 30% loss, right? So this is you know guys in retail who are also the market thinks are going to be losers because they they punish them. So the question becomes all right so when the market thinks you’re going to be disrupted do are you actually disrupted or do you tend to bounce back? Right? Um, and what’s interesting is first of all the medians. So what I show in this chart is the distribution of next one-year returns for the for this group of stocks relative that’s in the red relative to in the blue all stocks. And you can see that the medians are basically the same six or 7%. You go to average is about the same depending if you’re doing geometric whatever. The point being that like the the fact that you’re down on price alone says very little with regards to where you’ll be the next year, right? So just because software stocks are down today doesn’t mean we should all panic and say all right they must be zeros. It has very littleformational content with regards to the mean the median expected return over the next year. But if you look at the distribution this is where things get interesting. They’re very different obviously. So the blue line looks more it’s not normal. It looks more normal right? So all stocks tend to have a more normal distribution whereas disruption scare stocks have a really fat tail distribution super wide. So in fact um it looks like 10% of these stocks go on to double over the next year versus 3% for the full market. 16% go on to lose more than half versus 7% for the full market. So in other words the dispersion of winners and losers is so much wider for these guys these beaten down and disrupted stocks um both to the upside but also to the downside. when technology comes around it shovels the deck and you know the the entire you know the balls back you know the balls in the air and and what we’re kind of everything’s in play right and so I think that’s a really important um you know point to add to this idea of dispersion right so there’s you know dispersion in terms of historical historical returns future returns and then current valuations and all these things have just kind of blown out um due to the indiscriminate selling um and um you know just selling pressure and panic around around AI well I think This kind of really ties back to like the value traps versus emotes. Like clearly in this case, you want to avoid the the 16% or so that lose more than half. Um, and try to be on the, you know, the I guess the right side of the chart with the ones that survive. Right. Right. And and discernment, the ability to discern winners from losers matters more, right, in a time like today than it did historically or in an insulated sector. So what about what happens when we apply the in exhibit 29 when we apply the intangible value factor to those high scare dispersion stocks. Yeah. So just just to be clear like I’m using um intangible value as a because I already built on it like as a way of illustrating this point but the point’s more general. The point is more broad and but but I’ll explain I’ll explain the um the exhibit first and then we’ll we’ll get to the point. So what we see here is the returns which we already saw of the intangible value factor applied to the full universe in the blue and then the exposed sectors in the red and then what we do in in addition is to do disruption scare stocks and so remember the full universe think of like a bullseye right a dart board the the full universe is the is the widest circle exposed stocks are a subset of that and you know insulated being the other part of the subset and then within exposed stocks are disruption and scare stocks stocks that are both exposed and down 30% Right? And also you know investors or at least at the time perceiving them to be the uh the losers. And what you find is that the return for this factor as appi in applied to that final segment of disruption scare stocks is much higher than in the other than in the kind of wider circles. Right? So in other words, when you apply the intangible value factor to disruption scare stocks such as like software stocks today, but it could have been newspaper stocks in the past that the you know expost returns have have been higher, right? And and what is this saying, right? This is kind of your grin and con if you go back to like you know your your finance textbooks is that you know your you ultimately dispersion is something that allows you to amplify your edge. So for a given edge right um if you have a lot of dispersion in the market that means that your winners winners may will do better and your losers your shorts will do better too right and so like you know and this is also people people talk about this in the context of like venture capital or like um private equity like one reason why people love VC private equity historically is because um they’ve had high dispersion right and so therefore a given edge can be can be amplified over you know higher absolute return right and and again this this principle you know, generalizes from intangible value to any any edge. So, anyone who has an edge in picking software stocks in disruptions, right, which is again a big gift, but if you think you have a framework for picking that that works not all the time, but more importantly also specifically in times of disruption as such as today with software stocks, then this is actually a great time to be doing stock picking because high dispersion um is one of the things we don’t know. We think the mean will be the same, but the dispersion will almost certainly be higher. will will likely you know increase the returns to being able to separate winners from losers. I love that idea of the dispersion and the edge kind of coming together and that the intersection that you know that if you the high dispersion if you have any edge that’s when it can become you know possibly amplified. I think that’s a very great way to think about it and just conceptually I’ve never heard anybody explain it that way. So that’s pretty it’s interesting too by the way just on the human side of things like thinking about like the great software stock picker like they’ve probably got their best opportunity set they’ll ever see in their career right now. Yeah. Yeah. Because not only do they have an edge presumably in software, but there’s just a crazy amount of dispersion and you know there will be many companies many shorts will go to zero and many of the longs will be you know multibaggers right companies like that you know were sold down 60% that you know may go on to be the next Walmart of their sector right so you know very interesting time um you know to be to be a stock baker in software these days Kai your research is always super impressive and we’re very honestly privileged and um you know appreciative of you coming on with us and our audience and kind of working through um you know all this stuff if you were to and maybe we’ve already hit on the main takeaway I don’t know but if there if there is kind of a main takeaway uh you know from from from from all this research what would you say it is look I I think for many of these companies say software stocks I think the takeaway is that look the the code is not the moat right like for many of these companies code is one of the many things they do. But you know we as investors need to look beyond that to ask the question of what other intangible assets or just modes in general do these possess because if you go look historically you know based on all the works through prior disruptions um you know it turns out that um these other complimentary assets are you know potentially the the most important um you know indicator of which companies will survive and ultimately thrive through disruption. right now. Of course, AI adoption is important too, but you know, I think that, you know, doing this research over the past month or so has given me kind of a a deeper appreciation of the extent to which, you know, customer um loyalty, brand equity, human capital, network effect, these other emotes for software in particular um you know are are more important than maybe we initially thought when it comes to being able to survive a paradigm shift in the way um you know, technology works, right? and and and so so simply saying we’re going to buy stocks because they’re cheap, you know, I don’t think that’s sufficient. A cheap cheap cheap once they price the earnings, I don’t think that’s sufficient. Saying I’m going to buy these stocks because they, you know, have the most AI adoption. Now, obviously I’ve talked about that in the past and I do think that’s important, but I think that’s just it’s insufficient. I think really what’s come together in my mind more having done this research and especially bringing in the work of of David Tiss um you know has been the extent to which complimentary assets you know brands doing capital IP are are you know really quite quite important you know as we as we kind of think about which companies will ultimately be winners and losers long term um you know from the occurring selloff. Good stuff. Thank you guy. Thank you. Thank you for tuning in to 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 podcasts in the Excess Returns network at excess returnspod.com. If you have any feedback or questions, you can contact us at excess returnspod@gmail.com. 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.