We Study Billionaires - The Investor’s Podcast Network - TIP823: From Railroads to AI: The Timeless Patterns Behind Market Bubbles w/ Kyle Grieve

Episode Date: June 14, 2026

Kyle Grieve discusses what bubbles are, why they form, and why they always feel different in real time. He’ll examine historical patterns through frameworks from Insana, Kindleberger, and Howard Marks, and explain how investors can protect themselves by focusing on intrinsic value over narratives rather than speculation. IN THIS EPISODE YOU’LL LEARN: (00:00:00) Intro (00:02:31) Why understanding bubbles is critical for long-term investor survival (00:04:31) How “this time is different” fuels every historical bubble (00:05:37) Why smart money, incentives, and career risk inflate bubbles (00:07:21) How investors rationalize bubbles using new, useless KPIs (00:08:56) The predictable emotional arc: skepticism, euphoria, panic, collapse (00:10:04) Why price detaches from intrinsic value during bubbles (00:12:28) Kindleberger’s five stages: displacement, boom, revulsion, discredit (00:30:58) Lessons from tiny bubbles like plank roads and Beanie Babies (00:53:01) How human nature, not technology, causes recurring bubbles (01:08:44) How to protect portfolios from bubbles by focusing on value, not narratives Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Track ⁠⁠⁠⁠⁠⁠The Intrinsic Value Portfolio⁠⁠⁠⁠⁠⁠. Buy Trendwatching. Follow Kyle on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Related ⁠books⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Investor’s Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠⁠⁠sponsors⁠⁠⁠⁠: ⁠Plus500⁠ ⁠Netsuite⁠ ⁠Vanta⁠ ⁠Shopify⁠ References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor’s Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

You're listening to TIP. Have you ever lost money on a stock that was clearly in a bubble, but you didn't realize it until it was just too late? The most dangerous bubbles are the ones that never look obvious in the moment. Today, as we're seeing a massive shift in capital towards AI, I'm starting to hear a very familiar idea surface again. That belief is that this time is different. This topic fascinates me because, as an investor, I'm constantly trying to balance two

competing goals. I want to both capture as much upside as possible in my holdings, while also managing the risk of being exposed to a bubble. I don't really view bubbles as just some sort of theoretical risk. I see them as a very real and recurring threat that every single investor eventually faces no matter what they invest in. There comes a point where a rapidly rising share price feels exhilarating, but that excitement is often exactly when concern is most warranted. What's interesting about bubbles is that they're not really that rare and they're not unpredictable. They are simply a reflection of human behavior, combining things like greed, optimism, and social proof. While bubbles are often associated with transformative

new technologies, at their core, they're actually not about technology. They are about psychology. So today, we'll walk through Ron and Santa's framework for understanding how bubbles form. We'll explore the psychological forces that cause them to inflate so quickly. And more importantly, what exactly investors can take away from these patterns to help protect themselves for being swept up when the next bubble feels impossible to resist. So if you've ever been burned by a bubble before, or if you're wondering whether we might be in one today, this episode will give you a clear lens for gauging risk when excitement is running especially high. Now, let's get into this week's episode on bubbles. Since 2014, with more than 200 million downloads, we have interviewed

the world's best investors, studied deeply the principles of value investing, and uncovered many compelling investment opportunities. We focus on understanding businesses and intrinsic value, investing accordingly, and sharing everything we learn with you. This show is not investment advice. It's intended for informational and entertainment purposes only. All opinions expressed by hosts and guests are solely their own, and they may have investments in the securities discussed.

Now, for your host, Kyle Grubes. Grieve. Welcome to the investors podcast. I'm your host Kyle Greve and today we'll be discussing bubbles, what makes them and how to identify them. And most importantly, how to protect yourselves from the next inevitable bubble. To discuss this subject, we're going to refer back to a book called Trend Watching by Ron and Sana.

Ron has a very, very good foundation for how he perceives bubbles and he has a really, really good framework that I think is worth understanding and getting into a lot more depth into to help investors really just protect themselves from bubbles, which 100% of the time end up badly for those who bought at the top. Now, there are a few reasons why a framework for looking at bubbles is just so essential today. It's not just about AI hype. I'll save that for later, but I have a very personal reason for thinking about bubbles. Since I own a really concentrated portfolio, I have to manage all my holdings very, very closely and ensure that they still offer a reasonable upside. So if a holding of mine gets into bubble territory, it's much more likely

that I'm holding an asset that's about to go through a very precipitous decline rather than continue to climb up. Now, when something that I own suddenly serves in price, I get a mixture of excitement and fear. It's exciting to know that the market is seeing what you are seeing and that maybe your thesis is being validated. But it can also be pretty scary to see a business go up, you know, 5x in a year and understand that the story is still growing fast enough that maybe holding rather than selling is a better choice. The truth is, I want to understand this framework better so that I can protect myself. I never want to be the investor, you know, holding the bag after a 90% drawdown when it was very obvious that the stock's price was rising much faster than intrinsic value

would have indicated. And since these bubbles tend to follow very predictable patterns, patterns that are outlined incredibly well in this book, I think that recognizing them isn't just some sort of academic exercise, but that it's key to long-term survival. And since many listeners are in the same seat as I am, I figured it would be great to share the framework. So the first chapter of the book is titled, It's Never Different This Time. And I love the title because it feels like everywhere I look now, I'm seeing people saying just this. Every bubble that's outlined in this book or in other events follows an eerily similar narrative that this event is somehow different from the past. And because this time is different, investors can justify certain

things, whether that's buying at unreasonable prices or even using excessive amounts of leverage. So today, I hear many investors saying that the S&P 500 is not a bubble, which I admit I probably actually agree with. But what I see as being as more dangerous behavior is assuming that the S&P 500's PE ratio is a new base at which the index will live for into the foreseeable future. But here's the interesting thing. The PE ratio of the S&P 500 at the end of 2025 was 31 times. So if I remove the Magnificent Seven and I just keep the awful 493, which I recently saw it referred to as, it's only 19 times earnings. So this shows that the average American business just hasn't really improved that much. The index as a whole is just being propped up by a few outperforming businesses

that admittedly have some deep moats. Now let's get into the first chapter and discuss why people believe that this time is different in the first place. So the first primary reason is that every new generation believes that it's smarter than the last one. As technology gets better and more sophisticated, investors enter the market. And you'd think that investors have begun getting smarter, but as history shows, that's not really the case. Whether you're looking at the Great Depression, Japan in 1989, the great financial crisis, it's quite clear that investors believe that they had some sort of edge as time goes on, but that hedge still ends up in investors just losing their shirt when they ended up getting too greedy.

This reliance on technology does not mean things will change. It just changes the speed and delivery in which we make poor decisions. The second reason for bubbles is how the smart money can often create them. So, I think most of you know, it's the hedge funds that are going to control the largest amount of capital. And when their jobs depend on them, you know, keeping up or beating an index, they absolutely have to have capital in the market being put to work. And if they miss an opportunity to invest in a business that is maybe seeing a lot of momentum, them, they're literally increasing career risk. If the market is rising because, say,

Nvidia is up 34% year to date, then as a fund manager, you probably need Nvidia to be in your portfolio just to help you keep up with the market. If market sentiment remains bullish, it also directly affects your incentives. You'll make more fees and performance bonuses. So you want more bulls than bears, and this is something necessary for bubbles to form in the first place. And lastly, there's just, you know, client pressure. If you don't own the high flyers in a fund, then you're going to get redemptions from your partners who want to expose themselves to those winners. So if you don't own them, you risk losing capital for your own business. Now, the second reason things don't really change is that investors just remain in denial.

How do investors actively express denial in markets by rationalizing new metrics? For instance, during the tech bubble, many tech companies had very, very little profits and some of them didn't even have a functional product to begin with. So they invented new KPIs just to keep investors in denial. Some of these KPI included, you know, user growth and registered users, page views, engagement time, community size or network reach, burn rate, market share of markets that simply didn't exist, and then revenue run rates that were based specifically on monthly numbers. So these are all KPIs that investors used to value businesses and justify buying some of them

that were just, you know, pre-revenue and just didn't have very much fundamentals to them. But in reality, they were basically just using useless KPIs that investors, justified to bid up prices on speculative assets. So the third one here is how we behave and herd-like patterns. Investors start discussing assets as prices rise, not when they fall. The media is not going to highlight some boring, cheap energy prices today, but you're not going to find any shortage of articles about AI and how it's transforming businesses.

If you go to dinner parties, the average retail investor is probably going to be bragging about how they doubled their money on some random AI play, and not that they're buying some sort of value stock that's trading at 50% of its intrinsic value. So people who are skeptical about a new hot stock or asset tend to get drowned out, even if they have a valid opinion. The crowd and FOMO are two very compelling aspects of the market that keep us interested in just the wrong thing. Fourth, the emotional arc of a bubble is very predictable. We start with the herd being skeptical. They mostly refuse to take part, but a few people do.

Once more, the skeptics begin buying in, enthusiasm, just, just begins spreading like wildfire. And once there's just no skeptics around, the market becomes completely euphoric. At this point, all participants are in denial that there's a bubble, as a herd refuses to believe that they're part of one in the first place. And after that, comes the panic, where once euphoric people now panic as they want to avoid losing any more money. And this inevitably results in the assets price completely collapsing. Now, the emotional arc is also embedded with shifting paradigms. The book outlines a lot of technology that we now take for granted, things like turnpikes, canals, railroads, or even the radio. But at one time, these new industries

created by new technologies were just as top of mind as, say, AI, or quantum computing is today. With each new piece of technology comes a wave of investors who believe it will change the rules as it changes the world. But this just unfortunately never lasts, because some new technology is always waiting to be developed to steal the spotlight. So while innovation is great for society as a whole, as it usually means an improvement in our quality of life, it doesn't always mean great things for the underlying business that is actually developing the technology. Ford was the first large car manufacturer, but I don't recall any investor ever proposing that Ford was a good investment today. So what this all really comes down to is that human nature is the root cause of bubbles in the

past, just as it's going to be the root cause of bubbles in the present and root cause of bubbles in the future. greed, envy, and fear have all been present in humanity for thousands of years, and they're just not going anywhere. So if we intend to remain investors for a long period of time, it's vital to understand just how the market uses these emotions so we can hopefully protect ourselves as best as we can. And to best protect ourselves, we just really need to understand exactly what a bubble is. When I spent some time thinking about how I perceive bubbles, I knew I had a very different definition than in Sana. I think about bubbles in the context of business returns. If I own a business that's trading at, let's say, $1,000 today and I believe that it's

going to be worth $1,000 in 10 years' time, then there's a good chance that that business is currently in a bubble. In this scenario, I'm forecasted to have zero returns over a 10-year period. And this seems like a very foolish investment to me, doesn't it? This happens when the market refuses to price what an asset is worth, it prices it what people hope that it will become. A bubble is kind of a form of time distortion. Investors take all potential growth, future care.

cash flow and upside potential, and then they just cram it right into the present. Rod and Santa makes the point that bubbles are defined differently by different people. So, for instance, the former Fed chairman, Alan Greenspan, said that a bubble is an asset that without any external event declines by 30 to 40 percent in a relatively short period of time. An economist might say it's an upward movement of asset prices, which inflates conventional valuation metrics, then declines in value. Venture capitalist Roger McNamey said bubbles were when large amounts of capital,

were deployed into new and productive technology. NYU historian Richard Silla said a bubble is when an asset's price is wholly disconnected from the change in its underlying economic fundamentals. But I think you just get the picture across all these definitions. They all have to do with the assets price just exploding upwards and then crashing downwards for some kind of reason. Charles Kindleberger wrote the book Manias, Panics, and Crashes, which is on my bookshelf, but I haven't read yet.

But Insana broke down his five stages of a bubble. some detail and I think they're pretty key to understanding bubbles. So in Kindenberger's framework, the first stage is what he calls displacement. So this refers to an event that triggers the first rush into a particular asset class. This is when something like some sort of new technology or a fascinating innovation, maybe could be policy changes or even structural shifts that are creating excitement in the market. These events spark a reimagining of the future in a more rosy light. And this is where prices begin to be pulled forward.

The second stage is what he calls overtrading. This one is very simple. We can easily observe the amounts of shares traded daily for a given asset, especially in the public market. Sure, institutions can cause significant changes in trading volumes, but the real booms occur when retail investors help increase them. Overtrading and boom simply means that there are many more buyers than sellers, and that simply causes the assets price to skyrocket.

It's important to remember that over trading in a market context can actually be beneficial. say you own an undervalued asset, which is starting to increase trading volume, that can often mean that the price and value gap is actually closing. So purely using trading volumes to observe a bubble isn't a very good single indicator. The third one here is monetary expansion. And this was one of my favorite parts of the books, as we'll dive in more later, but easy money is a surprisingly large part of the formation of a bubble. Easy money does a few things. It makes it easier for corporations to secure funding to expand their businesses through things like improved technology. and innovations. And this creates a feedback loop. Once a bubble fuels a company, it lowers its cost of

equity, which can then lead to it using its own shares to purchase other businesses. That would also improve its technology further inflating its own bubble. So easy money also allows investors to borrow money to make investments. And when investors see that corporations are just exploding in value, they invest and they even use leverage on their assets to invest even more. This further inflates the value of companies that once again have that cheaper cost of capital. So the four stage here involves popping of the bubble. So Kindleberger calls it revulsion. Revolgent may not happen overnight.

It can actually take a bit of time. But it's basically when the bubble initially pops. Some investors, but not all, might see this as a time to just add to their position. For instance, Isaac Newton actually added to his position in the South Sea company after the price dropped 20% only to see the majority of his investment completely wiped out shortly thereafter. So the revolving period can often be completely invisible to the casual observer. Institutions might quietly exit their positions, selling to retail investors, and not trying to create a big scene, which could easily spook retail investors.

But either way, this period is when investors begin to become disillusioned with that new technology. The fifth and final stage is called discredit. So this is simply when sentiment does a complete 180. The asset that was formerly loved by all is now hated by all. This can take time or happen nearly overnight. Constellation Software recently had a 56% drawdown between May of 2025 and February of 2026. And while I don't think that was a bubble, it definitely was a business that moved very swiftly from being loved to hate it. Now, going back to my definition of a bubble, Insana mentions terminal value and how that becomes completely distorted during a bubble.

For those who are unfamiliar with terminal value, it's basically the discounted value of their future cash flow. So here are two bubble-like businesses that were completely disconnected from their terminal values. The first one was Yahoo during the tech bubble. This business basically required 18 billion customers just to justify its current stock price. And keep in mind, this was in the year 2000, so the actual population was just a fraction of this. The second was RCA, or Radio Corporation of America, and this happened before the Great Depression. So from 1923 to 1929, the business's price went from $5 to $600. And interestingly, RCA is still around today, but it actually took 36 years to read.

to previous all-time highs. Now, RCA here is very interesting because I think it shows that a very successful business can still go through these bubbles. I tried getting some of the data for it and during this period where it went through this incredible rise in price, net income actually compounded at 35% annually. So the company was continuing to improve. But the problem was just the market and how it perceived those improvements. So in 1923, the stock traded for just 15 times earnings, which sounds very reasonable if you knew that the business was going to compound its net income at 35%. But by its peak, that multiple exploded to 285 times earnings. So that was where the real problem was.

Now that we know the psychological makeup of a bubble and how bubbles tend to form, let's go over some of the lesser known bubbles in the U.S. I was just actually stunned by how many there have been and how many were outlined in the book. Bubbles in the U.S. are definitely not a modern phenomenon. When most investors think of bubbles, I think they really tend to think of things like the Great Depression, maybe the go-go years, the tech bubble, or the great financial crisis. But this book shows that bubbles go back in America much, much further back. Bubbles tend to feel modern at the time, but then look trivial a few centuries down the line.

For instance, the U.S. had a bubble in things like turnpikes, plank roads, canals, and even in bicycles. These are all things that we don't think at all as a technological marvel today, but they were at the time. So in 1847, the U.S. was actually going through an infrastructure boom, and that's when pamphleteers were trying to to convey the potential in some of these investments to potential investors. One such innovation was in plank roads. So what the heck is a plank road, you may ask? It's literally a road that's made of wooden planks rather than, say, gravel or dirt, which is what the alternatives were at that time. Now, the market for plank road companies was starting to heat up. Between 1847 and 1857, 1,388 plank road companies incorporated in 17 different states. And they offered investors a pretty

hefty 10 to 40% annual dividend. It's important here to put your business owner's hat on for a second, ask yourself, if a business where competitors are literally everywhere, how can they actually offer any type of competitive advantage that their competitors can't offer? So let's say you were in New York at the time. I think the answer to that very simple question was obviously they can't. New York still had 340 Plank Road companies alone. So how did these pamphleteers encourage investment? To fully understand that, we need to look at the modern equivalent of a lot of the pamphleteer. And that is basically just a stock promoter. You don't see this as much on large and mega cap names simply because they tend to be so well known that they just don't require any

more promotion to increase investor interest. But smaller companies tend to get promoters. And there's a sort of cottage industries for certain companies to go out and promote a company's business. But often they're simply just trying to manipulate stock prices. It's an ugly business, but I've seen it. I was recently at an investing conference where I was approached by someone multiple times during the conference, to go and listen to a company's presentation and talk with management, which I had zero interest in doing in the first place and even less in doing after. I had heard this promoter continuing to get me to talk to them. Let's take a quick break and hear from today's sponsors. Curious about online trading, but haven't taken the first step yet. You're not alone. And plus 500

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security and compliance and earn and prove trust. Get started today at vanta.com slash t-I-P. That's v-a-n-ta.com slash t-I-p. All right, back to the show. So I would say that too much promotion is definitely a red flag. Well, let's go back to the 1850s. Promoters in today's days were those pamphleteers. Instead of broadcasting, you know, a tweet, a substack article, or a YouTube video, they handed out pamphlets. The pamphlets promised things like large returns on investment, which drove obviously a ton of interest. And the reasoning they gave was just a complete fiction. They claimed that wooden planks had durability to last 10 to 15 years, when they would only last on average, maybe four years or so.

They overlooked the downside of plank, such as horses falling through and breaking their legs. But as the bubble grew, investors started wondering just where the return was. And the businesses weren't really showing that they were able to validate the marketing from their pamphlets. The roads ended up breaking early and required very costly work and supplies just to maintain. A great case study of the Plank Road investment comes from an investor named John Taylor. So he invested $900 in three local plank companies in the Albany, New York area. Over a 12-year period, his combined dividends totaled less than $80. This implies a dividend yield of only 0.7% per year, a far cry from that 10 to 40% that were being promised by the promoters.

One $250 investment that he had into all. Albany and Rensel-Araville Plank Company was sold for only $25. And another of his investments just never paid a dividend simply because they had to spend too much money on repairs. Taylor writes that by 1865, the majority of Plank Roads were either abandoned or connected to turnpikes. This example shows the dangers of combining hype, oversupply, and poor economics. It's also a case study showing that these three ingredients just tend to completely wipe out investors.

And it's a solid example of Kindleberger's framework. The first one being displacement, so the reduced prices of transportation was obviously seen as this kind of new revolution for commerce. And this resulted in thousands of miles of Plank Roads being proposed, funded, and built. And then you look at over-trading. As a bubble built, more businesses traded for higher and higher evaluations, even though the business plan didn't really support any value creation. Third is monetary expansion.

As the Plank Roads were built, they received funding from investors to continue building more. Fourth is revulsion. As people like John Taylor showed, they eventually just left the idea simply because it wasn't really offering any real returns, just a broken promise. And five is discredit. These businesses were rightfully, I think, discredited because they just completely eviscerated capital, which is why they were eventually abandoned. Now, I think it's obvious that hype cycles like this are being seen today.

Quantum computing is another area today that I think is completely ripe for a bubble. Rigetti computing at one time in 2025 was up nearly 12x in under a year. And what fundamental results from the company precipitated this massive increase? My only answer? Hype. Since 2022, revenue and EPS have decreased while margins continue to decline. The only answer for a 12x in a share price is that investors are relying on hope and the greater fool theory just to make their return. Just like the Plank Roads of the 1850s, investors are putting money into a capital destroyer simply because it tells an interesting story.

Now, the Plank Road bubble, I think is a pretty good example of what Ron calls tiny bubbles. These are bubbles that will only affect a pretty small number of the population and won't cause Jerome Powell to lose any sleep. In other words, tiny bubbles aren't system-wide bubbles. A $200 million micro-cap business trading at maybe 300 times revenue might harm a few investors once that bubble pops, but it's not going to cause panic in the global financial system. But tiny bubbles still obviously matter a lot to investors because while they may not ruin the global financial system, they can easily ruin your portfolio. If a single investor has exposure to a business that is part of a tiny bubble, when it pops, the market may not notice at all, but I guarantee you that the investor will.

So what are some good examples of these mini bubbles? And Sana go over things such as closed-end funds, which I think are a great example. So first thing first, what is a closed-end fund? It's basically a mutual fund that purchases a basket of stocks representative of the stocks in a given market. So he goes over European and emerging market closed-end funds. It's important. It's important. important to recognize that close-end funds are not ETFs. Therefore, the prices of closed-end funds can often be completely disassociated from the net asset value of the asset. So what happened in Europe to create a bubble in close-end funds? Countries like Germany, Austria, Spain, and Italy all saw very large spikes in the prices of their close-end funds peaking in 19, which coincided

with the fall of the Berlin Wall, which speculators thought would maybe create some sort of piece-time dividend. And these funds attracted many foreign investors who wanted exposure to a country's corporate world. The problem was that many of these funds were trading at over, you know, 150% of their net asset value. So if you simply had just bought the stocks on the open market, you would have received a massive discount compared to that country's closed end fund. But the closed end fund allowed investors to just have exposure to these markets without really having to do any due diligence on specific companies. Now, when the bubble popped in the early 90s, many of these closed end funds dropped significantly in price. German closed end funds

declined by 75%, Austria, 80%, Spain 60%, and Italy, 65%. Now, another example from the book was the Cuba Fund. This was a closed end fund in the U.S. meant to give American investors exposure to Cuba once it reopened for business to Americans. Since Cuba was illegal for Americans to invest in, the fund came up with very, very creative ways to get exposure. These included things such as investing in Florida-based and Caribbean-based companies that maybe had a chance of benefiting from doing business in Cuba if it was eventually open.

for business. And apparently a lot of the capital in the Cuba fund was actually just in T-bills, which were accumulating interest in anticipation to invest at a later time. So tiny bubbles don't even actually have to happen in public markets. Insana points out that collectibles are another area where tiny bubbles have formed in the past. Beanie baby dolls are a great example of this. So Beanie babies had some of the best marketing gimmicks that I've ever seen. And the mastermind behind the marketing strategy was this gentleman named Ty Warner. So he created scarcity by retiring certain models of these Beanie Babies each year, effectively decreasing the supply, which created this supply and demand imbalance. To help drive interest in Beanie Babies, he do kind of these product tie-ins with

businesses such as McDonald's or have these giveaway nights at professional sporting events. And one of his biggest schemes was to announce that Beanie Baby production would completely cease at the end of the 20th century, which further increased the buying pressure. The book outlines that the price of Beanie Babies went up a thousand times its face value at its apex and have now stabilized for decades. But I'm actually not sure that the bubble ever fully popped. A quick search on eBay shows a number of Beanie babies still selling for over $10,000. For instance, the Princess Diana BB Baby Baby Baby is listed for $19,500 today. The key lesson here regarding big bubbles or tiny bubbles is to think about whether you are truly investing, which is based on capturing a gap between price and value, or if you're

just speculating and focusing purely on price. Buying a doll for $20,000, $20,000, seems pretty obvious that you're probably just focusing on price. You want to make sure that you are focusing on the fundamentals and not necessarily purely on the narratives that people are telling you. Just like stocks, any asset can rise and quickly collapse in price. So make sure you're allowing Benjamin Graham to whisper interior about the importance of investing versus speculation. Unfortunately, tiny bubbles are an excellent trap for retail investors. And that's because participants feel early. They feel highly intelligent. And that unfortunately, creates this velocity in price increase that only reinforces their illusion. But if you partake

in too many tiny bubbles, you can easily end your career as an investor. Now, as a concentrated investor myself, I'm fine having positions that go up and make up a larger and larger percentage of my total portfolio. But it's also essential to have some sort of diversification embedded in the portfolio. Don't be the guy who loses a bunch of capital, then goes all in on one asset hoping to make up your loss. That's a great way to just destroy wealth. So even if you are, concentrated and focus on the fundamentals, make sure that you have a few positions to spread your risk into. And it's always important to remember that there's no such thing as a risk-free investment. The next bubble is arguably the largest in modern history, and that's the tech bubble of the

1990s. And what stands out to me most about this bubble is a massive participation of the American public. This bubble wasn't just professional investors bidding up, you know, railways. It was your average household pouring money into the market via 401ks, mutual funds, and, you know, just day trading. Another good indicator was to look at bank deposits. When bank deposits tend to be high, it generally tells you that the market is in no rush to spend or invest money. But during the tech bubble, bank deposits fell to 50-year lows, all while stocks grew to 58% of household financial holdings. The widespread participation injected a massive amount of liquidity into tech stocks and made the eventual collapse of the market all the more painful for everyday people.

Now, I don't really believe the narrative of the tech bubble was much different from other past manias, whether you look at things like railroads, automotive industries, cars, turnpikes, canals, or even beanie babies, where the big difference was in this mania was just how early the market was. The tech bubble was built around the narrative that the internet would change the world. And it did. But that value creation unfolded over many decades and not over quarters. But the market priced those expectations as if they were just around the quarter. Now, the extreme inclines and valuation do a very good job of underlying the disconnect between

price and value. So in March of 2001, before the bubble burst, the NASDAQ was trading at 246 times earnings compared to a historical range that was closed at only 40 times. Now, a six times premium to historical averages isn't just a minor stretch. It's basically impossible to justify a premium that high for an entire index. And here's the thing. There's nothing wrong with a company getting a multiple re-rating once the market, you know, verifies that the business is improving. But it's unheard of for a market to improve at that rate across a very broad number of companies. Then when you consider that many companies in the index just didn't even have working products, you begin to see a picture where the multiples were going up while the average quality of the

businesses inside of that index were actually declining. So in 1999, economist Robert Samuelson concluded that 77% of IPOs had no profits. The speculative energy in the market just was inflating valuations across the entire NASDAQ. But there are other forces at play here as well. The Fed reduced interest rates to help troubled banks recapitalize themselves and pull themselves out of an economic slump. And this created reduced borrowing costs, which are obviously key ingredients to a bubble. Energy costs had also come down freeing up money to be spent elsewhere, such as gambling and stocks. Wall Street had its part in the euphoria as well.

Since many of these dot-com businesses couldn't be evaluated using traditional value-creating metrics such as free cash flow or profits because they just didn't have any. So investors on Wall Street created new metrics. Instead of analyzing the future cash flow of these businesses, they looked at things such as page views and unique visitors. And there wasn't any shortage of new opportunities for investors to look at either. Another key sign that markets are in bull mode is to look at IPOs. So IPOs from 1980 to 1989 average about 311 IPOs per year.

Now, from 1990 to 2001, that number jumped to 380 per year. So after the tuck bubble exploded, the average dropped closer to 200. Now, what of Monash-provised biggest multi-baggers was explained as a case study in this book? And this is CMGI, where Insana saw a failed internet incubator. After the fact, Monish saw an opportunity before the fact. Now, I'm not sure how much of a multi-bagger CMGI was for Monish, but what the business was was basically an internet holding company. So it would basically supply internet companies with capital and then take them public. It was essentially just a public venture capital company.

Monish wrote this idea at the exact right time and got out before it cratered from around $140 to just $5 in early 2001. Now, the problem with CMGI was that it was just not a sustainable business model. It required a bubble in internet stocks to function. And any business that requires an eternal bull market is just not going to last very long. Now, while Insana liked Kindleberger's framework about bubbles, he actually had his own spin on it, which I think is actually an improvement. If an investor has this framework, they could theoretically speculate on a bubble and try to get out before everyone else does. I think that this is maybe what Monash was trying to do with the CMGI bet.

Now, to help you better understand how to deal with bubbles, let's go over Insana's updated version of Kindleberger's framework. So Ensona's framework differs from Kindleburgers as it focuses much more on the rise of the bubbles, whereas Kindleburger's, framework focuses on both the rise and the fall. So here are the five ingredients of Insana's bubble. The first one's called the eureka moment, which is when the world makes an exciting discovery or invention. Two is easy money. This is when there's a low cost of money and high availability of cash and credit. Third is government largesse. This is where there's favorable economic conditions or maybe tax incentives that are put in place by the government. Fourth is auspicious economic conditions. And fifth is an external stimulant. Now we've already come.

covered many discoveries and inventions such as Plank Roads, but you can also include other things, of course, such as railroads, cars, the internet, or even mortgage-backed securities. Next is easy money. So when there's cheap credit, corporations can fund growth at a cheaper cost. This means they're more likely to borrow and spend even more. When a business can spend more on investments, there's a chance that they can create additional shareholder value, which, of course, shareholders love. When investors show a disposition to deploy their savings, more money comes into the market, which can further improve dealmaking. So if a company has a high valuation, it can then use its stock as currency to make additional deals or to invest in itself.

Now, government large-est includes things such as tax incentives, subsidies, and new policies that can supercharge an industry. For instance, during the railroad boom, there were these land grants that were granted to railroad builders. During the internet boom, there was a tax moratorium. So these seemingly small events can have very outsized effects down the road. when other entrepreneurs come in and try to take advantage of them. Next are auspicious economic conditions. Look, you know, bubbles don't happen when a country is in a recession. They happen when things are going well, when GDP growth is strong,

when unemployment levels are low, and when consumers are optimistic and willing to open their wallets and purse drinks. If unemployment levels are high, chances are a bubble isn't going to happen simply because money isn't plentiful. And the last part here is about external stimulants. This can include macro factors such as war, regulatory changes. demographic shifts or even crisis. These stimulants can accelerate demand and attention. What I like to look for are what I call micro stimulants. So in one of my investments that didn't

quite work out for me called BQE Water, the thesis centered on their selenium product, which could reduce selenium levels in effluent water. So the stimulant for this was simply that North America would no longer allow selenium to be deposited into waterways at high levels. And BQE had an excellent product that would directly help with this issue. And it was back. by patents. I like these kind of stimulants. Now, just like Howard Marks, who I'm going to discuss in a little more detail here shortly, Insana believes that we can detect bubble-like events through pattern recognition. His pattern recognition framework is what we just covered. It's important here to know that we can never predict things with 100% certainty. The best hope we can have

is to recognize the recurring conditions in which bubbles form. So the first three ingredients necessary for an asset bubble to form are technological innovation, easy money, and government at large-est. To identify the things, you just simply have to observe the world, you know, read newspapers, look at industries and individual companies, analyze economic indicators, and maybe consult with experts. Insana says that if you can identify those three ingredients, you can be ahead of the curve in finding high potential investments. But if you notice an industry with these three ingredients that are truly creating value, how do you make sure you protect yourself once the bubble pops? While I have no desire to participate in bubbles, I'd actually just prefer that my businesses

public market valuation simply increase at the same rate as their intrinsic value. But in reality, that's just not what happens. Stocks go up and down with nearly 50% variance on average each year. And sometimes they can obviously go up much, much higher than that. So with that in mind, investors must always watch to make sure an investment isn't in bubble territory. What does Insana say to look out for when a bubble is getting into this terminal phase? So he notes a few key indicators to watch for. Money turns tight, fiscal policies that once incurred, investments are repealed. Innovations, inventions, and discoveries fail to deliver on their initial promise, underlying economic conditions deteriorate, and public participation peaks, which is a signal

that the bubble is about to pop. Now, the easiest way for money to go from easy to tight is just when interest rates rise. Rising interest rates means higher borrowing costs and less corporate profits. So that's an easy signal that any investor can really look for. But it's also important not to get scared out of a position that's maybe not in bubble-like territory just because you see rising interest rates. So while I think rising interest rates weren't a cursory glance, they need to be considered alongside other signals to be valid. Next is fiscal policies. So Insana argues that fiscal policy that was released before Black Monday in 1987 was actually one of the reasons for that crash. The story here is that much of the boom leading up to Black Monday was actually fueled by the

leveraged buyout boom. And these LBOs were funded by something called junk bonds. A bill was then introduced that actually repealed the junk bond deductibility, which sent buyout firms down. Now, when we analyze the failure of delivery of the benefits of new technologies, we don't have to look much further back than the example which I already gave, which was the tech bubble. So many investors were drawn to the potential profits that were promised in the narratives of these businesses. But once it became completely apparent that these narratives were more dreams and reality, investors began exiting in droves. Now, the breakdown of economic conditions can be seen in a more recent event, such as the Great Financial Crisis. During that time, credit froze, asset prices

declined, the housing market fell, global GDP declined, and U.S. unemployment rose to 10%. The bubble in mortgage-backed securities was no longer sustainable, so it eventually burst. During the Great Financial Crisis, there was no obvious bubbles that formed during that time, simply because economic conditions were not set up to create one. Finally, we reached the final signal, which is public participation. One of my favorite anecdotes on this came from Peter Lynch. He said he was surrounded by people at a dinner party. In stage four, once again, they're crowded around me.

But this time, it's to tell me what stocks I should buy. Even the dentist has three or four tips. And the next few days, I look up his recommendations in the newspaper, and they have all gone up. When the neighbors tell me what to buy and then I wish I'd taken their advice, it's a sure sign of the market has reached the top and is due for a tumble. There isn't much more to add to this. Now I do think that all these signals are important to look for, but just because a segment of the market is in a bubble does not mean that the entire market is.

When the market is expensive, I like to look at my own portfolio and see which of my assets have risen the most in price. Then I compare it to the increases in the intrinsic value of those businesses. I have a few positions, for instance, that have doubled in 2025, but does that mean that they're in a bubble? don't think so. I think it just means that they're probably a little bit overpriced. Now, in this case, some investors might take profits and some, like me, just do nothing. When you have an asset that wasn't cheap to begin with and maybe went up five times in price

and has only increased its intrinsic value by, let's say, 10%, then you need to get very, very worried. One experience I had with this exact kind of scenario was a business called in mode, which I no longer own. So this business makes minimally invasive aesthetic devices. Now, from the depths of COVID when I initially bought it until just late 2021, less than two years later, the company was doing exceptionally well. It doubled its EPS in that time. Let's take a quick break and hear from today's sponsors. Curious about online trading, but haven't taken the first step yet. You're not alone.

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Plus 500 makes trading more accessible than ever. Check it out at Plus500.com. Trading and futures involves risks of loss and is not suitable for everyone. Not all applicants will qualify. Plus 500. It's trading with a plus. Every business is asking the same question. How do we make AI work for us? Sitting on the sidelines is of course not an option.

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It's AI built into the system that runs your business. And if I had needed this product, it is exactly what I'd use. If your revenues are at least in the seven figures, get their free business guide, demystifying AI at netsuite.com slash TIP. The guide is free to you at net suite.com slash TIP. That's net suite.com slash TIP. Before I join the Investors podcast, every what if you can imagine was running through my head. What if I'm not the right fit for this audience? What if I freeze up on camera? What if I can't keep up with the level of analysis people here expect? Well, betting on

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I unfortunately carried it through and didn't sell anywhere close to the top of the bubble, but luckily I still made a good return simply because I got out before the business really started to stagnate. Now, let's continue with the point here that tiny bubbles can happen, but sometimes they are precursors to maybe some hidden dangers. And that danger has to do with fraud. The unfortunate part about bubbles is that fraud can flourish when oversight tends to maybe be a little more lax and when investors euphoria is intense. So the late 90s were a time of rapid growth and renewed optimism based on tech. These rising markets helped weaken skepticism among key parties, including auditors, analysts, banks, and investors. At this time, Enron was just thriving because the increasing complexity

of the business was mistaken for intelligence. And since the stock price continued to rise, investors just threw caution to the wind. Now, one of the problems with Enron, according to investor perception was that its complexity was seen as a feature and not a bug. Enron positioned itself as this innovator in energy trading and broadband. And even though the business was really peddling commodities, it created a narrative base around how it was transforming itself into a tech company. Now, during this time, one of its key markets was deregulated. California recently deregulated its market allowing Enron to sell power and trade electricity in one of the largest markets in the U.S. Now, unbeknownst to regulars at this time, Enron was just manipulating energy prices in California,

which ended up costing the state about $11 billion, but the regulators didn't even catch onto this until after Enron went bankrupt. Now, the issue here with Enron was that they were really, really good at hiding its fraud. They hit it from regulators and auditors. So, you know, the average investor just didn't have a chance. So what exactly was Enron doing? They did things such as booking Imagine Profits as current income. they used off balance sheet debt to make their balance sheet look healthier than it was in reality.

And then they had off balance sheet partnerships which Enron Insiders secretly ran. And they counted trading transactions within their own company. And they allowed those fees on those transactions to count as their own revenue. So in a typical environment when everyone wasn't getting rich, this may have raised questions. But in the increasing bubble at that time, these issues were just kind of swept under the rug. So my big lesson here is to just not get complacent in bull markets. Fraud happens all the time in public markets. The key is to remember to stay vigilant.

It's when emotions are running high and your intuition tells you to just be lax, is probably the time when you should actually be the most vigilant. Now, the final chapter of trend watching deals with potential bubbles in the future. The book was published in 2002 before the great financial crisis. But interestingly, Ron directly discussed real estate as a possible bubble. So he created a checklist with his own framework. External shock? Yes.

monetary stimulus, yes. Fiscal stimulus, yes. Economic conditions, favorable. Speculative public participation? Not quite yet. So he was already starting to see some of the writing on the wall that perhaps inside of real estate, a bubble was beginning to form. And that final part, speculative public participation needed just a few more years to fester before things ended up blowing up. So according to Y charts, U.S. housing homes sales didn't peak until around 2006 at 7 million homes sold. That was up from just 5 million in 2000. And after the bubble popped, sold homes

normalized back into about a 4 million range. So I would say public participation indeed reached a very speculative fervor, but not until 2006. And even after that data was public, it still had a lag time before fear gripped the market as a great financial crisis didn't actually start until mid-2007.

Now, if I'm being completely honest, I don't think about bubbles, much using Ron's specific framework, simply because I subscribe to Peter Lynch's notion that if you spend more than 13 minutes analyzing economic and market forecasts, you waste it 10 minutes. So I tend not to spend too much time on this at a macroeconomic level because I think my time is much, much better spent looking at the impacts on each of my individual businesses. I have a grocer in Poland and I have a trust

builder in Canada and they're just not going to have much macroeconomic concerns in common. Instead, I look at what concerns in each of those countries can contribute to the business. businesses booming or declining in the future. Now, I think it's definitely worth using in Sona's framework here in businesses that you own where maybe the businesses themselves or maybe the businesses that are associated with them begin increasing in price very, very rapidly. But if they aren't really showing a rapid increase in price, there's no real need to use this framework because you're probably just going to scare yourself and there's much, much better uses of your time. So a potential bubble is forming today that I think is very fascinating.

Now, while I don't have direct exposure to it, I do have indirect exposure. So it does take some of my thinking time. And the rest of this episode will be devoted to discussing AI and whether or not it's in a bubble using Insana's framework. So throughout this episode, we've gone through a number of different bubbles and we've traced the recurring patterns that happen in each of these bubbles. So nearly every bubble is driven by things like human behavior, technological innovations, access to easy money, which all ends up culminating in speculative excess. And while AI takes a few of these boxes, I'm not sure we're all the way there yet. Now, there is no way that I can know AI is in a bubble with 100% certainty. All I can do is use the tools available to observe where we are today, which at least will

inform me where we could be headed in the future. But as Insana's call on the great financial crisis showed, you can still be mostly right, and yet the bubble won't plot for many years ahead. Now, where AI is strongest is in the general interest from the media and from investors. The fascinating thing about AI is that nearly anybody can touch it. It's not some sort of abstract thought or idea just floating around. It's tangible. You can easily go and use AI right now to improve your efficiency, learn something new, or automate a process.

One interesting area of AI to ponder is just what kind of bubble it could be. So Howard Marks outlined two types of bubbles. The first is inflection bubbles, which are in some ways good bubbles. These are bubbles that deliver truly transformative technology to humankind. So whether or not investors win or lose money is another story, but the underlying technology remains a positive for mankind. Railroads and the internet are great examples of inflection bubbles. The second is mean reversion bubbles, or just simply bad bubbles. These are the types of bubbles where the markets tend to rise and drop precipitously,

but there's actually no added benefit to society. So I think it's pretty evident that AI probably falls into the inflection bubble category. And while you can consider inflection bubbles to be good, that definitely comes with a cavvary. out that I already mentioned here. And that's that you can still lose money investing into transformative technology that is actually transformative. But the reason that inflection bubbles can be considered good is just because of all the interest in that technology that it creates. Scientific progress ends up being compressed from decades to just years. Now, the thing with AI like many other technological marvels from the past is that there's just no simple historical benchmark to try and

draw wisdom from. Is AI like railways or the internet? There's no way to know on. unless you can put yourself in a time machine and go into the future. The uncertainties in AI involve a few questions. Things like who's going to be the biggest beneficiary of AI? Which of today's leaders in AI infrastructure buildout will be overturned, like the early social media companies such as Myspace? Will AI profits go to vendors? Or will they be competed away by price wars?

And will AI products be the specialized products, or will they just be treated as commodities? The next area of exploration is regarding monetary policy. The thing about economic policy is that the past monetary policy can have second order effects that happen years down the road. So if we rewind back to 2020, 2020, 2021, when interest rates were rock bottom, this had several effects. Money was easy to borrow and retail investors flush with cash could invest it into the market.

So today's AI buildout can be partially explained by the lagging effects of that added liquidity that we ended up getting post-COVID. And part of that is that companies funding it are also just wide moat companies that are flushed with cash in their balance sheets and generate cash from their operating business. But the build out still can't be solely financed with cash. So JP Morgan analysts believe that the current cash outlay for AI infrastructure will cost somewhere around $5 trillion. But companies like Microsoft, Alphabet, Amazon, Meta, and Oracle only have about $350 billion total on their balance sheets. So while part of it can definitely be funned with cash on hand, there's going to be significant amounts of leverage that are going to be required to build it out here. Oracle, meta, and Alphabet just issued

30-year notes with average coupon rates around 5.7%. Current 30-year treasury yields are around 4.7%. So investors in those bonds are only getting 1% returns above treasury yields, which is a pretty low spread given just how uncertain the results will be from these investments. Junk bonds right now yield around 7 to 8%, but don't have these 30-year terms. So money right now isn't the cheapest it's ever been. We aren't in, you know, that zero interest rate world anymore, although you can argue that the Magnificent Seven's cost of equity has gone down, as those businesses tend to trade at premiums. But there are other deals out there that maybe do make it appear that money is easy to get a hold of. So Marx highlighted a deal in his recent memo. So a company named Thinking Machines,

which is an AI startup by former Open AI executive Mira Muradi just raised about $2 billion in its seed round at about a $10 billion valuation. Yet, the company doesn't have a product, nor do they even tell investors what plans they have for a product. So one investor went to a pitch with Maradi, and Maradi said, we're doing an AI company and with the best AI people, but we can't answer any questions. And if you think that's completely wild, with this first seed round taking place sometime around October 2025, there's actually news today that the same startup is now seeking another

funding round valuing the business at $50 billion. Another former open AI scientists raised $2 billion for his company, again, with no product valuing it at $32 billion. So perhaps money is cheaper than what treasury yields are telling us. Now let's turn our attention to government large S or fiscal stimulus. Here I think it's pretty clear that the government is indirectly subsidizing the A.O. buildout via things such as semi-conductor and incentives. and cloud infrastructure spending. So in the U.S., the Chips Act authorized nearly $53 billion in federal spending to support U.S. semiconductor manufacturing and research capacity. This has helped chip companies like Micron, Intel, TSM, Samsung, and Texas Instruments fund U.S.-based

fabs and R&D labs. The EU has a similar Chips Act with $16.5 billion in funding. China has spent an undisclosed amount on shoring semiconductor manufacturing and on acquiring new technology. So, you know, around the world, governments are fighting to get ahead in this game. And while these investments aren't directly tied into AI, they are indirectly as the AI buildout requires these advanced chips. Where the government is spending money, investors will continue to flock to. So now we get to two areas where I think AI is a little weaker as viewed through Insana's framework. So the first of that bubbles are most likely to form under favorable economic conditions. So what are economic conditions like today compared to five years ago?

I would say they are probably less favorable. Interest rates are higher, which increases borrowing costs. The U.S. government hasn't distributed handles lately, like it did to address the pandemic. So a lot of the fiscal stimulus has kind of faded away. The GDP growth in the U.S. in 2025 was just 2.1%, which is the lowest since 2019, except during the COVID recession. Unemployment rates are currently around 4%, which is higher than any year since 2019, other than the two years immediately after COVID-19.

So I wouldn't say economic conditions today are the best they've been. The last factor is public participation and speculative behavior. I would say with some of the private deals I've already highlighted along with these 30-year bonds that I spoke about, there is undoubtedly some speculative behavior going on. Firms with no products being valued at $30 to $50 billion within months definitely strikes a speculative note. But looking at IPO markets, which is an excellent indicator of bubble-like behavior among the masses, doesn't really show much evidence of a market-wide bubble.

2025 IPO proceeds were $38 billion. This pales in comparison to $21, $142 billion. And while the IPO market has heated up since 2022, it still lags the years preceding COVID. And the absolute number of IPOs is just slightly above the average for the decade, but nothing too alarmingly high. Now, you could argue that the increase in the Meg 7 prices is part of a public participation in the bubble. After all, these are companies that are just gushing cash and they're investing in the future of AI. The S&P 500's results look much different from those of its equal weighted counterpart.

So the total returns to the S&P 500 has been around 19% per year compared with just 11% for the equal weighted S&P 500 index. But even when you look at the evaluation of the Meg 7, I would say as a whole, yes, it's expensive, but I would be very, very hesitant to say that it's in bubble-like territory. Sure, you're paying a premium to the index, but these businesses, you know, they have better quality, they have wider modes, and they're growing faster than the average American in business as well. So they deserve to have a premium. A company like Nvidia, which trades at a trailing 12 months price earnings multiple 44 times, is expensive, sure. But analysts estimate it's

going to grow EPS nearly 60% next year, which helps explain that high evaluation. Now, it's important to remember that expensive doesn't mean bubble. If you look forward to a business like Nvidia, analysts actually believe that it will continue to grow EPS well above 50% compounded into 2027. So if I look at the forward PE of Nvidia, it's around 24 times, which is actually lower than the 27 times of the S&P 500. This just doesn't really scream bubble to me. But here's the thing. Bubbles can pick up momentum very quickly. And if history is a precedent, there are plenty of businesses inside the S&P 500 or that are even currently private.

That can create a narrative based around it transforming itself into some sort of AI play. Now, some of these businesses maybe are actual AI plays, but some of them will probably. probably try to take advantage of the speculative markets by claiming they're an AI business, when in reality, they're just putting lipstick on a pig. So how can we protect ourselves if AI remains top of mind for the market and as the economy improves and mass participation becomes a reality? First, we must recognize that bubbles are simply a part of investor psychology. Whether you want to label AI as being in a bubble or not is much less important than understanding just how investors are behaving around it. So pay attention. Read the news.

talk to other investors and observe how investors are behaving towards AI and AI stocks. If you think that AI is headed towards a bubble, some investors might see that as an opportunity to maybe try and get involved and try to time the market. They may tell themselves that they can get out of a position that begins getting into bubble-like territory before the market does. But this is a pretty challenging game to play. Even if you're right on the bubble, there's no way of knowing just when it's going to form or how your own perception of it will change over time.

So make sure you size your positions right, stay disciplined on your evaluations, and don't allow your ego to inflate, which can cause several problems down the road. Next, you need to separate three layers of risk. Layer one is a technology. AI will truly be a transformer of technology. Maybe the biggest technological breakthrough of my lifetime. But it's vital to remember that technological success does not equal investor success. My general strategy is to avoid investments that are spending money on the AI buildout.

To me, that's just pure speculation. I'd rather invest in businesses that are already leveraging AI capabilities today. The second layer is the business. When it comes to who's going to win the AI race, anyone who claims to know the answer, I think, is probably just a liar. In the big bubbles of the past, many entrants tried to win the market, but very few actually ended up surviving over time. Let's just look at the automotive sector in the U.S.

So in the early 1900s, the U.S. market had hundreds of car manufacturers. And that's because there are just no clear winners, just like AI today. In cars, there are no barriers to entry and there was rapid innovation. Sounds an awful lot like these AI startups that are starting to pop up everywhere. But today, there are only three car manufacturers, four General Motors and Chrysler. The survival rate here was less than 1%. So if you're looking at an AI investment,

you have to ask yourself whether the business that you're investing in is truly a business that has some sort of competitive advantage or if it's just purely based on hype. And my guess is the majority of these new businesses, is popping up are going to be based on hype. The third layer is to analyze value. This is where bubbles form. When investors are willing to pay higher and higher prices for an asset, bubbles form because new investors come in thinking someone else will buy it from them at, you know, an even higher price. The stock price embeds things like optimism, which is why it's imperative to understand it deeply. The businesses that I own are exposed to AI, such as, you know,

Lumine and Topicus, are already businesses that are really profitable. They have recurring revenue engines. So I'm very comfortable holding them. They're not some sort of, you know, speculative pre-revenue company which could just as easily go to zero as it could go to 100x. So I have to think long and hard about what they're valued at and if there's some sort of mispricing that I can take advantage of. One strategy that I like to use here is to just view data from a business or industry's historical averages. We did this exercise already on the NASDAQ earlier. But if you're looking at AI companies, consider the historical averages for their earnings and cash flow multiples. And then just compare them to today. Are they below?

in line, or above historical averages. Beware when the multiple is double its historical average. The next point of concern is once again related to value. You may have found a business that is utilizing AI to turn itself around. As a result, profit margins are set to explode, and the historical PE numbers maybe just aren't as helpful as they once were simply because the business model has fundamentally changed. Now you need to give yourself a terminal value reality check. So I like doing this when I'm looking at basically any business that I own. So if I have a business that I think can grow at 15% per year, what does that company look like in five years? So let's use Lou Mine as a very simple example. In order for them to continue growing their intrinsic value

around 15%, they're probably going to need to add something like three to five new acquisitions per year. Maybe that scales up over time. But let's just use that three to five year number here for simplicity's sake. So in five years, that would be a total of 15 to 25 acquisitions. Now, when I think hard about that, and I think is that possible for Lumine to do? The answer is very easy. Yes, I think they can do that quite easily. So if we contrast that now to a business such as thinking machines and it's $50 billion valuation, what really needs to happen for the company to grow into that valuation? Even though I'm not a fan of valuing businesses on revenue multiples, my guess is that a business like this is probably going to be evaluated on that while they figure

out how to turn a profit. So an ARR business growing, let's say 20% per year, easily fetch a 10 times ARR multiple, especially in an industry with tailwind and a lot of media coverage. So in that case, it needs to generate $5 billion in revenue over the next few years. Is that possible? I have zero conviction of my ability to make that decision, which is why I have no skin in the game. So whenever you're looking at any business, whether this AI or not, I would recommend sense checking what you think it can be worth in the future. If it makes no sense such as that Yahoo example that I gave earlier where it required 18 billion users when the world's population was just a fraction of that, then you know that the evaluation has become incredibly

stretched and may be in bubble territory. The next thing you can do is try to identify bubble behavior. Howard Marks mentions that he's no AI expert in his latest memo, which I'll link to in the show notes. But he is a master at observation. He's someone who likes making these kind of deal-making vignettes to help him better understand investor psychology. This allows him to determine if he should be aggressive or conservative. Now, given the AI examples that I've covered here, I would say that being overly conservative towards AI businesses today is probably an excellent idea. Now, if you want to move away from more subjective observations, such as, you know, looking at bubble like behavior, then just look at objective measures. So I already went through some of the deals involving

debt for AI companies in public and private sectors. So look at other deals and see what other businesses are paying for debt and for mergers and acquisitions. Does the price that they're paying defy reality? Then that's a great signal to just take a pass. So what does all this really mean for how I actually manage my portfolios. It's quite simple. The first thing is that I cap my exposure to narratives and I focused as exclusively as I can on the fundamentals. Now, I know I'll screw up on that because it's impossible not to get caught up in narratives at some degree, but you have to try hardest to focus on the fundamentals of the business. The second is to ensure that my winners are growing specifically because the intrinsic value of those businesses are compounding and not purely

because of multiple expansion. If I have a business that I held over five years and intrinsic value didn't grow at all, you get the share price went 10x or 5x even, that's pretty scary because basically that just means that there's a narrative around the business and more and more investor interest is being generated by that narrative and not by the changing fundamentals of the business. And the third one here is to just simply observe deals that are going on in your company's industries. Do they seem reasonable? Or are they based on dreamy narratives? If narratives are propping up a business or an industry that your business is involved in, be extremely cautious. Selling might be the best idea, even if it's a company that you really, really like.

Thanks so much for spending time with me today. If you'd like to continue the conversation, please follow me on Twitter at Irrational MRKTS, or connect with me on LinkedIn. Just search for Kyle Grief. I'm always open to feedback, so please feel free to share with me how I can make this a better experience for you. Thanks for listening and see you next time. to TIP. Follow the Investors podcast on your favorite podcast app and visit the investorspodcast.com

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