But I knew that in the world of investing, critical judgment and nuanced decision-making matter more than anything else. These are things that, at least for now, AI still struggles with.

What AI excels at, I believe, is quickly processing massive amounts of data; basically in real-time. It’s a powerhouse when it comes to retrieving information, cross-referencing details, and delivering results almost instantly. But the question of whether it can truly make informed, strategic decisions – that’s where things get tricky.

At its best, AI isn’t about replacing human thought, but augmenting it. In investing, the value of AI comes not from replacing the decision-maker, but from enhancing their ability to gather, process, and analyze information quickly and thoroughly.

In this post, I’ll take you through how I’ve been using AI – specifically NotebookLM – in my investment research process. I only recently started leaning into NotebookLM I must admit, but over the past few weeks, this tool has been an absolute game-changer, helping me move through the complexities of analyzing businesses faster and with more confidence.

While I wholeheartedly agree with Tiho’s take below, and just like him believe AI can’t replace the critical thinking necessary for assessing the true potential of an investment, it can help me build a much clearer, more detailed picture of the companies I’m interested in, and find answers to the high-quality questions (at least that’s what I hope) I come up with.

At its core, what I’ll show you here is how I leverage NotebookLM to get up to speed with a business more efficiently, to dig deeper into critical factors like a company’s competitive advantages, and to improve the overall quality of my decision-making process.

The Challenge of Getting Up to Speed with a Company

When I first dive into researching a company, the goal is simple: get up to speed as quickly as possible, absorb the relevant details, and make sure I have a deep understanding of what’s going on behind the scenes. In the fast-paced world of investing, time is of the essence.

I encourage you to watch this recent Druckenmiller interview, in which the idea of getting up to speed with a company quickly may be more important than ever before, frequently pops up:

Sure, Druckenmiller is right that time is important (even more important is timing fwiw), but I believe even more critical than time, and timing is accuracy – ensuring that I’m gathering the right information in the right way. What’s the point of forming an opinion quickly, but being wrong? That’s a recipe for losing money very quickly.

Traditionally, this process involved a lot of manual labor. The first step was always to gather the most recent filings – annual reports, quarterly earnings calls, investor day presentations. Then, I’d sift through analyst reports, industry publications, and competitor information. You can imagine how time-consuming that can be. Hours and hours … With the flood of data coming in from every angle, it often felt like I was drowning in information but not necessarily getting any closer to actionable insights.

The challenge wasn’t just about gathering the information – it was about managing it. How do you ensure that you’re not missing any key details? How do you quickly sort through the noise to focus on what’s important? How do you strike that balance? And, with so much to read, how do you avoid spending hours or days without making significant progress?

This is where AI comes in. Most investors quickly realized that AI could be a powerful tool to streamline this process. Instead of spending countless hours sorting through reports, AI could help aggregate, synthesize, and analyze data from multiple sources much more quickly. But I knew it wasn’t just about speed—it had to be accurate too.

That’s where NotebookLM came in.

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Unlike traditional large language models (LLMs), which can often “hallucinate” (incredibly dangerous in investing!) or provide inaccurate or fabricated information, NotebookLM offers a more reliable approach. While LLMs are powerful at generating text, they sometimes struggle with maintaining accurate context over longer pieces of content or complex queries.

I used prompts such as …

“In this chat, you are only allowed to use the materials I upload. Don’t draw on outside knowledge or your training data.“

… but it never really worked.

This issue is referred to as “context rot” – a concept I only recently heard about. Essentially, the further into a conversation or analysis you go, the more prone the AI becomes to losing track of important details, leading to errors or inconsistencies. Also, LLM can't reliably extract information from the middle of long contexts (which is exactly what I’m doing by providing multiple 50-200-page resources) – i.e. the information is there, but the AI doesn’t access it; without telling you!

NotebookLM, on the other hand, is designed to handle more structured, context-rich data. It can manage multiple layers of information simultaneously and keep everything in context without losing track of key details. This eliminates the problem of hallucinating, ensuring that the insights I get from the AI are grounded in real data and more accurate. This reliability is what makes it such an invaluable tool for serious investment research.

I put together some general attributes of NotebookLM below:

• You can upload PDFs, Google Docs, text files, slide decks, web links, and even YouTube video transcripts into a notebook.

• Each uploaded file (called a source) can be up to 500,000 words or roughly 200 MB in size.

• Free plan (standard NotebookLM on a Google account)

  • Maximum of 100 notebooks total.

  • Up to 50 sources per notebook.

  • Daily usage caps such as 50 chat queries and 3 audio overviews per day.

  • Deep Research reports limited (e.g., ~10 per month under the free tier).

• Paid plans (via Google AI Plus / Pro / Ultra tiers)

  • Notebooks: often up to about 500 total under Pro/Plus.

  • Sources per notebook: up to 300 or more on paid plans.

  • Higher daily chat/query limits (e.g., hundreds instead of tens).

  • More daily audio/video overviews and Deep Research sessions.

My Research Process: Creating a Foundation with AI

When it comes to building a solid foundation for analyzing a company, the research process has to be methodical. For me, that means breaking down a business into its most critical components and diving into each of those areas separately. This isn’t a quick glance at a balance sheet or a snapshot of a quarterly earnings report – it’s a deep dive into the company’s business model, products, customer base, competitive advantages, and so much more.

I’ve always worked with specific checklists for each area of analysis. These pillars of research guide my thinking and help me avoid missing any key details.

The idea is simple: get a comprehensive picture of the company from all angles. These checklists cover everything from the business model to product offerings, competitive advantages, risks, growth drivers, management, and more. This multi-dimensional approach ensures that I’m not simply looking at the company through one lens but understanding it fully in terms of what drives value.

Example of a checklist item:

I can say whether the business has a high customer retention rate (low churn rate; the longer a customer is retained, the more predictable the revenue is, while low retention pressures the company to acquire new customers constantly).

• How long does the average customer stay with the business? (the longer a customer is retained by a business, the more profitable that business becomes and the more predictable a firm’s revenue; in comparison, a company with a low CRR needs to be constantly acquiring new customers with exciting new sales strategies, expensive marketing campaigns, or unique product offers).

• Have I conducted a customer-based corporate valuation (CBCV) analysis?

  • I can say how high the customer acquisition cost is (CAC).

  • I can say what the customer lifetime value is (CLV)

  • I have applied this concept to the business: “The idea of customer lifetime value (CLV) has been around for decades. CLV equals the present value of the cash flows that a customer generates while they are engaged with the firm minus the cost to acquire the customer. The present value of cash flows, in turn, is a function of sales, costs, and customer longevity.” (Michael Mauboussin)

  • What’s the average purchasing frequency of customers?

  • What’s the average basket size – i.e. how large is each order? (“If the first order isn’t profitable, the business needs multiple orders to recoup its CAC and even more orders to produce a profit. When the first order is profitable, additional orders run up the scoreboard. This is why repeat rate matters.”)

But here’s the catch: even with a well-structured research process, the amount of data involved can be overwhelming. This is where AI, and particularly NotebookLM, really starts to shine. AI is fantastic at taking those initial piles of data – whether it’s quarterly reports, annual filings, or industry research – and organizing it in a way that makes it easier to analyze and makes information much more accessible. I’ve found that NotebookLM does an exceptional job of quickly processing and synthesizing all the material I need.

So when I start digging into a company nowadays, the process looks something like this:

1. I start by using more traditional AI tools like Google Gemini or ChatGPT (I haven’t found one or the other model to be clearly superior in terms of deep research quality for whatever it’s worth) to generate deep research reports on each of the pillars (8 in total) I have in my framework (with the help of my checklist). I’ve found that my checklist is so comprehensive that generating separate deep research reports (vs. just one “big” one) leads to a higher quality of the “research.” For example, I might have one report focused solely on the company’s business model, its unit economics, how it generates revenue, etc., another on the competitive landscape and management team, and another on the growth drivers going forward (I’m simplifying things a little here). Each of these reports provides a detailed look at a particular aspect of the company, and from there, I can start drawing connections between them. Once I have these reports, I input them into NotebookLM as my starting base.

   

2. Company filings are a critical next step. I always add recent press releases, quarterly and annual reports, investor presentations, proxy statements, and earnings call transcripts. I’ve found that these documents, which can be time-consuming to read through manually, become much more manageable when fed into NotebookLM. The AI not only helps organize them but also highlights key takeaways, trends, and critical changes over time – something I might have missed if I were reading them all one by one.

   

3. Finally, I round out the research stack by incorporating industry reports, sell-side analyst notes, and any other write-ups that I find valuable. These external perspectives provide context and nuance that help round out the internal view I’m getting from the company’s own filings.

Two Notebooks per Company

To ensure that I’m getting the most objective and relevant information possible, I actually create two separate notebooks for each company I research. The first notebook is dedicated solely to primary sources – things like 10-Ks, 10-Qs, 8-Ks, and proxy statements. This notebook serves as a foundation for the company’s most thorough, official filings – documents that management is legally required to provide. By relying on these primary sources, I can eliminate any subjective opinions or biases that might arise from secondary sources.

The second notebook combines both primary filings and secondary sources. This includes “secondary” materials like the aforementioned deep research reports, blog posts, write-ups, podcast transcripts, or industry articles. While secondary sources can be more subjective or even speculative, they often provide important context or alternative perspectives that can complement the hard data found in primary filings.

Once all of this is in place, I have a solid base of information to start working with. It’s like building a house: you can’t start making decisions on the structure until you have a foundation. In this case, that foundation is built with company-specific filings, industry insights, and deep AI-driven research reports.

From here, I can begin asking more specific, strategic questions that help me get deeper insights into the business’s competitive position, management team, growth potential, and risks.

Next Steps

At this point in the research process, the groundwork has been laid. I’ve gathered the necessary filings, external reports, and deep-dive research on the company. But now comes the real challenge: how do I take this foundational knowledge and apply it to gain a deeper understanding of the most important aspects of the business?

This is where NotebookLM, combined with other AI tools, becomes invaluable – not just as a data organizer, but as a key partner in sharpening my analysis.

The AI excels at refining the way I think about the company. Sure, it can sift through the reports and summarize them, but the true benefit lies in how it helps me think about the information. It becomes a tool for focused inquiry. As investors, we’re always trying to ask the right questions, and AI can significantly improve how I frame those questions. Rather than just passively absorbing information, I engage with NotebookLM to ask pointed, strategic questions about the business.

For example, one area I might want to understand more deeply is a company’s competitive advantage. How does it defend its position in the market? What barriers to entry exist for competitors? These are not trivial questions, and getting the right answer requires looking at multiple facets of the business – from product differentiation to customer loyalty and brand strength. By feeding my initial research into NotebookLM, I can ask the AI to generate deeper insights into these topics, pulling from both internal documents and external market data.

What’s interesting is how AI can help me not just with direct answers, but with exploring different angles. For instance, if I’m digging into a company’s growth drivers, the AI can suggest areas of the business I may have missed. Maybe there’s a regional expansion strategy or a new partnership that’s buried deep in the filings. NotebookLM helps highlight these opportunities by cross-referencing data and pointing me toward potential areas I wouldn’t have considered on my own.

But NotebookLM doesn’t just respond to questions I ask; it can also suggest its own. Sometimes, the AI will offer insights into areas I hadn’t thought to explore. I’ve found these suggestions incredibly valuable at times.

Let’s say I want to analyze the management team. The AI can scan all relevant public information about the leadership, their past performance, and their track record at similar companies. From there, I can ask more specific questions like, “Has the CEO been successful in navigating downturns in the past?” or “How does the company’s leadership style align with its long-term strategy?” This level of targeted inquiry helps me break down the leadership dynamic in a way that’s more granular and less surface-level than a simple bio or background check. Here’s another example from my recent deep dive into the London Stock Exchange Group:

The key here is that NotebookLM isn’t simply a data aggregator – it’s a dynamic tool that fosters a back-and-forth dialogue. As I continue to work through the information, I can adjust my approach, ask deeper questions, and adjust my focus based on new insights. AI is a partner in this process, helping to uncover hidden aspects of a business that might otherwise go unnoticed.

Another powerful aspect of using NotebookLM in my research process is its ability to calculate specific metrics automatically, saving me an enormous amount of time and reducing the risk of mistakes. In the past, calculating business-specific metrics (think of adjusted margins, certain ratios, etc.) would involve manually gathering data points from multiple sources, using calculators, and constantly cross-referencing information. It’s a tedious and error-prone process. However, with NotebookLM, I can input all the relevant data, and the tool will handle the calculations for me. For instance, I recently used this feature to calculate the M&A sales multiple for Topicus, which revealed critical insights about the company’s valuation.

The ease and accuracy of this process allowed me to focus more on the strategic analysis, rather than getting bogged down in number-crunching.

Refining My Understanding

Once I have my foundational research in place and have begun asking the right questions with the help of NotebookLM, the next crucial step is refining the analysis. Investment research is not a one-and-done process – it’s iterative. As new insights emerge, it’s essential to adjust your understanding and reframe your questions to dig even deeper. The beauty of using AI in this context is that it makes this feedback loop faster and more efficient, ultimately leading to a more nuanced and informed investment thesis.

Sometimes, I’ll also grab my phone and chat with a different AI model – usually something like ChatGPT or Google’s Gemini. I’ll ask questions that come to mind on the spot, whether it’s about a company’s recent performance, how a competitor is positioning itself, or even general market trends. This kind of real-time, informal querying helps me stay connected to the flow of information without getting bogged down by formal processes. While NotebookLM offers the deeper, more structured analysis, these casual conversations with other AI tools add another layer to my process and help keep me agile and allow me to stay on top of any emerging questions or ideas that pop up throughout the day.

So again, ultimately, the entire process described so far is not just about gathering more information or arriving at some kind of finish line – no, it’s about continually refining one’s understanding, refining the questions, asking better questions, and digging deeper into the nuances of what’s really driving the business.

For example, if I find that the management team is performing exceptionally well in one region but struggling in another, I’ll ask the AI to explore what factors might be influencing those discrepancies. Is it a leadership issue? Cultural differences? Different CAC/LTV dynamics? Or is the business model not scaling effectively in that region?

What’s key to remember is that this process never stops. The stock market is dynamic, and as new information becomes available, the research process and your judgment must evolve with it. AI plays a crucial role here, allowing me to continuously update and refine my understanding with minimal effort.

Instead of starting from scratch each time I uncover a new piece of data, I just insert it into the already existing nootbook and “chat away.”

AI in the Investor’s Toolbox: It’s Not a Replacement – It’s a Tool

As AI continues to evolve, many investors may find themselves wondering if these tools can truly replace human judgment. After all, in a field as nuanced and unpredictable as investing, the human touch is often seen as irreplaceable. My experience with NotebookLM has confirmed this: AI is not here to replace the investor’s role, but rather to enhance it. AI tools are best understood as assistants, not decision-makers.

I still do the heavy lifting when it comes to critical thinking, intuition, asking the right questions, etc. These are the aspects of investing that require experience, deep industry knowledge, a “feel” for market sentiment, and an understanding of broader economic trends – areas where AI, despite its impressive capabilities, can’t yet compete with human experience and judgment.

It’s my job to analyze the broader context, assess risks, and make the final call. AI simply helps me get there faster and with more clarity.

The real power of AI lies in its ability to sift through vast amounts of information at lightning speed. This allows me to spend less time on the boring grunt work – like gathering and organizing data – and more time on the strategic, high-level analysis.

However, I never let the AI make decisions for me. The responsibility of interpreting the data, asking the right questions, and understanding the broader picture still lies with me. AI can suggest new angles, uncover hidden risks, or offer insights into a company’s operations that I might not have noticed. But in the end, it’s my expertise and judgment that make the final call.

This partnership between human and machine is what makes AI so valuable – it accelerates the research process without diminishing the need for human oversight.

In short, AI is a tool – one that can augment the investor’s capabilities, but not replace them. As long as we remain aware of the limitations of AI, it can serve as a powerful ally in the investment process, helping to unlock new insights, streamline research, and support faster, more informed decision-making.

PS: I’m not using any of the features from the Studio panel btw (they’re fun to play around with, but don’t provide much value for my process).

But here’s the thing – while the P/E ratio is popular, it’s far from perfect. In fact, it’s often misleading.

As investors, we are constantly looking for efficient ways to evaluate companies. The P/E ratio seems to provide that efficiency – it’s easy to calculate, easy to pull up (and chart) on investment research terminals, easy to understand, and is frequently quoted in news articles and stock commentary.

But there’s a significant catch. While the P/E ratio might give us a quick snapshot, it hides crucial details about a company’s growth prospects, financial health, and risks.

In this post, we’ll take a deeper look at the 10 most common pitfalls of the P/E ratio and explore why relying on it can be dangerous.

We’ll examine some of the key factors that the P/E ratio fails to account for. We’ll also dig into real-world examples to illustrate how the P/E ratio can mislead investors, especially in complex, high-growth companies.

By the end of this post, you’ll hopefully have a clearer understanding of why the P/E ratio is just the tip of the iceberg when it comes to company valuation. We’ll discuss alternative ways to think about a company’s true worth—ways that provide a more comprehensive and accurate picture of its potential.

Part 1: 10 Common Pitfalls of the P/E Ratio

The P/E ratio might seem like a straightforward tool, but it hides a wealth of critical information about a company’s true financial position, and can thus be incredibly misleading. While it often gives a rough indication of how expensive or cheap a stock is relative to its earnings, it fails to account for several important factors that can dramatically affect a company’s future prospects and value.

Let’s break down the ten key pitfalls of the P/E ratio tat I’ve identified for this post, starting with what it overlooks.

1+2) Reinvestment Rate, Reinvestment Runway & ROIIC

One of the biggest limitations of the P/E ratio is its inability to capture how much of a company’s earnings can be reinvested into the business at attractive rates, crucially, for how long this can continue, and what returns (ROIIC) it generates on each additional dollar of invested capital. In fact, we’ve packed to metrics into one here.

Because two businesses earning 20% on incremental invested capital in the next five years can follow completely different trajectories if one of the two can only reinvest all of its capital at this rate for the next two years before it has to lower its reinvestment rate to 70% to maintain the 20% ROIIC.

ROIIC x Reinvestment Rate is arguably one of the most important formulas in investing. Yet, it is rarely covered by the financial media or “educators.” Companies with high ROIIC on reinvestments – meaning they can generate strong returns on new investments – have a greater potential for superior long-term growth.

However, the P/E ratio doesn’t tell you anything about how much capital a company is reinvesting in its operations, acquisitions, or other opportunities, and at what ROIIC. A company that can reinvest earnings at a high return can justify a MUCH, much higher multiple because its future growth is higher, more sustainable, and predictable.

On the flip side, if a company’s reinvestment opportunities are dwindling, its growth prospects become less certain, even if its earnings are currently strong.

And even two companies growing at the same growth rate do not “deserve” to trade at the same “fair” multiple. Because if one company arrives at 20% growth by reinvesting only 50% of its earnings power (40% ROIIC) and another one gets there by reinvesting all of its profits (100%) at a ROIIC of 20%, the first company generates more distributable cash!

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So, without factoring in the reinvestment rate AND the ROIIC in future investment, the P/E ratio provides an incomplete picture of a company’s true value. A company might look cheap based on its P/E ratio, but if its ROIIC is low or declining, it’s a sign that the company is not efficiently using its capital to grow. Conversely, a company with a high P/E ratio but also high ROIIC could be worth the premium, as it shows the business is reinvesting wisely and generating strong returns.

3) Capital Allocation

The P/E ratio also overlooks a company’s approach to capital allocation, which is another vital element for long-term success. Capital allocation refers to how a company decides to use its resources – whether through reinvestment in the business, acquisitions, share buybacks, debt reductions, or paying dividends.

A management team that is skilled in capital allocation can make all the difference between a company’s success or failure. However, the P/E ratio doesn’t shed any light on how effectively management is using the available capital – is it creating tons and tons of value or quite literally setting shareholders’ money in fire?

A company that reinvests wisely and selectively buys back stock at an opportune time might be a good candidate for an investment, even if its P/E ratio looks higher than its peers.

But if the company is inefficient in deploying capital – say, by overpaying for acquisitions, or making large capital expenditures that don’t create value (not all growth is valuable!) – the P/E ratio will fail to alert you to these risks. The decision to allocate capital intelligently is a key factor that will drive a company’s long-term performance, and yet it is invisible in the P/E ratio.

PS: I guess one could bring up stock-based compensation, the rate of dilution, and actual per-share profit growth as well in this segment.

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4) Earnings Quality

Another serious issue with the P/E ratio is its reliance on reported earnings, which can be distorted by various accounting practices. The quality of earnings can vary significantly between companies, and relying on earnings that are artificially inflated or deflated can lead to misvaluations.

For instance, a company might report strong earnings due to cyclical peaks, such as a booming market or high commodity prices. But this can be misleading if those earnings are not sustainable in the long term. Counterintuitively, cyclical companies tend to be the best investment when they look the most expensive.

The P/E ratio, however, treats these high earnings as if they reflect the company’s true earning potential, ignoring the fact that those numbers may be cyclical or inflated by one-time events.

A more effective approach would be to look at earnings adjusted for non-recurring events or to evaluate companies based on steady-state earnings (future profitability once growth reinvestments have stopped) and/or mid-cycle earnings (adjusting for embedded cyclicality).

But the P/E ratio doesn’t distinguish between cyclical peaks and attractive growth reinvestment opportunities, which can lead to faulty conclusions about a company’s valuation.

5) Durability & Moats

A company’s moat – its competitive advantages – plays a major role in its long-term success. The P/E ratio, however, doesn’t account for the durability of these advantages or the potential risks of disruption by new competitors.

Companies with strong, durable moats – such as a powerful brand, network effects, or regulatory barriers – can command higher valuations because they are better positioned to sustain profitability over time.

On the other hand, companies without a strong moat or with weak competitive positions might face significant challenges that could erode their earnings over time, even if they have a favorable P/E ratio.

6) Tail Risk

The P/E ratio also fails to account for tail risk – the possibility of rare, catastrophic events that can drastically affect a company’s valuation. In financial markets, tail risks are events that have a very low probability but can cause significant damage when they occur. Examples include regulatory changes, technological failures, technological disruption, or unexpected economic shocks.

If a company’s future earnings are highly vulnerable to such events – such as a heavily regulated industry or a business model exposed to unpredictable shocks or a business relying on a single product or just 1-2 key customers – the P/E ratio does not provide any insight into how these risks could affect its valuation. These black swan events might be overlooked entirely when assessing a company based solely on its P/E ratio, which is based on historical earnings and does not factor in extreme, low-probability outcomes.

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For example, a company in an industry facing rapid disruption may be trading at a low multiple, but the P/E ratio would overlook the full scale of risk it faces from new competitors, and even that low multiple may still not be “fair.”

7) Probabilistic Modelling

Investing is inherently uncertain, and the future is rarely predictable in a linear way. Yet the P/E ratio treats earnings as if they are deterministic, based on the assumption that current profits will continue at the same pace. This approach ignores the range of potential future outcomes.

Probabilistic modelling, on the other hand, involves considering multiple scenarios and assigning probabilities to different outcomes. This approach gives investors a better understanding of the potential risks and rewards a company faces, rather than relying on a single estimate of future earnings. The P/E ratio doesn’t take this into account, which means it fails to factor in the inherent uncertainty of the business world and the unpredictability of future events.

Here’s an example of a recent scenario-based valuation attempt I recently shared in one of my deep dives:

To learn more about this topic, read this post next:

8) Opportunity Cost

Lastly, the P/E ratio doesn’t consider opportunity cost – the idea that the value of a company should be evaluated in the context of its alternatives. For example, a stock trading at a 20x P/E might seem reasonable in a low interest rate environment, but in a world where interest rates rise to 5%, that same multiple might look excessive. The opportunity cost of investing in a company with a high P/E ratio in a high-rate environment can be substantial, as investors could achieve better returns elsewhere.

9) Tiho Brkan’s Concerns on Pattern Recognition in Unstable, Nonlinear Fields

In addition to the standard pitfalls of the P/E ratio, Tiho Brkan, a critical voice in the investing community, has raised some very important points about how pattern recognition can mislead investors in unstable and nonlinear markets.

One of Brkan’s main arguments is that pattern recognition works well in stable and linear fields. In these fields, past events and data points can be a reliable predictor of future outcomes. For example, in chess, where the rules are fixed and the variables are constant, identifying patterns can lead to successful predictions and strategies.

However, the stock market is not a stable, linear field. It is an unstable and nonlinear environment, where past performance doesn’t necessarily predict future results. The stock market, like many complex systems such as military strategy, geopolitical events, or even public health, is characterized by nonlinearity – where cause and effect aren’t always directly connected and where outcomes can be counterintuitive.

In such environments, traditional methods like the P/E ratio, which rely on identifying patterns in historical earnings, can be misleading. The P/E ratio simply doesn’t capture the full complexity of market dynamics, and using it as a shortcut for stock valuation can create a false sense of security. Investors might mistakenly assume that the patterns observed in a company’s past earnings will continue into the future, overlooking the possibility that the environment has changed, or that new, unpredictable factors are at play.

10) Overconfidence Bias and the Misuse of the P/E Ratio

Another critical insight from Brkan is the concept of overconfidence bias, which is particularly prevalent among seasoned investors. Overconfidence occurs when investors, especially those with experience, place too much trust in their tools and frameworks, assuming that what worked in the past will always work in the future. Brkan argues that many investors, after years of using the P/E ratio and other multiples, become overly reliant on these methods, even in a rapidly changing market environment.

As Brkan highlights, experience – while invaluable – is not the same as expertise. Overconfident investors, particularly those relying on traditional multiples, may misapply their knowledge, leading to costly mistakes. In a dynamic and volatile market, overconfidence in the P/E ratio and other valuation tools can create blind spots, preventing investors from seeing the bigger picture.

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Part 2: Business-Specific Pitfalls

While we’ve discussed many of the broader pitfalls of using the P/E ratio, there are certain business-specific factors that make the metric even more inadequate.

In the real world, no two companies are alike, and the factors that influence one business’s valuation can differ significantly from those of another. These factors – ranging from complex accounting practices to unique business models or complex balance sheets – can make the P/E ratio a particularly misleading tool in some cases.

When dealing with companies that have intricate financial structures, aggressive acquisition strategies, or unique operational models, it becomes evident that traditional valuation multiples fall short.

These business-specific pitfalls often cannot be neatly categorized in a general list, as they require a deeper, often qualitative, understanding of the company’s specific context, strategy, and financial nuances.

One example of this that I recently covered on the blog is Topicus, a serial acquirer in the VMS space with a complex ownership structure and accounting practices that make its financial statements particularly difficult to interpret.

Below, I’ve excerpted a section from my deep dive on Topicus, where I discuss how the P/E ratio can be misleading when applied to such a company:

Why The P/E Ratio Is a Trap

If you pull up a standard financial screener and look at the price–to–earnings ratio for Topicus, you are likely to see a number that looks either terrifyingly high or completely nonsensical.

It is a common mistake. I see investors dismissing the company because the “multiple” doesn’t fit their value framework, but they are using a broken compass. The income statement of a serial acquirer like Topicus is intentionally designed to be “ugly” from a GAAP perspective. It is a byproduct of how they grow. If you rely on net income to value this business, you are doing it wrong. Here’s why:

The most recent source of confusion is the way Topicus handled its investment in Asseco Poland. As I mentioned earlier, the transition to the equity method in September 2025 forced a massive, non–cash revaluation loss of €221.7 million onto the books. In the eyes of a simple P/E screener, that looks like a collapse in profitability. In reality, it was just a technical reset. The company didn’t lose a cent; they simply changed the way they labeled the asset.

When you add the complexity of majority–owned subsidiaries like Sygnity, where you have to subtract non–controlling interests, and consider the complexity of Topicus’ ownership structure itself (go back to part 1 to fully understand the non-controlling interest associated with the shares held by entities like Joday and Ijssel), the “bottom line” becomes a hall of mirrors.

You are looking at a mix of fully consolidated revenues, proportional profits from associates, and subtractions for minority partners. The income statement is trying to tell too many stories at once.

Another reason the P/E ratio fails here is the heavy burden of amortization. When Topicus buys a software company, they have to allocate a huge chunk of the purchase price to “intangible assets” (we discussed this in our balance sheet analysis) – basically the value of things like the software code and the customer relationships. They then “depreciate” these assets over several years. This creates a massive non–cash expense that pulls down net income.

Is the software actually losing value that quickly? Usually not. In many cases, the mission–critical nature of VMS means software solutions will be used for decades, yet the accounting rules force Topicus to pretend they are evaporating. This is a gift for the company because it reduces their taxable income, allowing them to keep more cash for the next acquisition. But for the investor looking at the P/E ratio, it makes the company look much less profitable than it really is. It’s a classic case where a “bad” income statement is actually a sign of a very healthy, tax–efficient cash machine.

I also want to briefly touch on the working capital dynamics, which is perhaps the most underappreciated part here. Because Topicus mainly sells subscription software (as a reminder, maintenance fees account for approximately 70% of revenue), they often collect payment upfront at the beginning of the year (“Many of the businesses invoice customers for annual software maintenance fees in Q1 each year resulting in a disproportionate amount of cash being received in the first quarter as compared to the remaining three quarters […]”). This creates “deferred revenue” – a liability on the balance sheet that hasn’t been recognized as income yet.

You receive the cash today, but you only get to report the “earnings” slowly over the next twelve months. This creates a beautiful disconnect. The cash is already in the bank and being used to buy the next company, but the income statement hasn’t been given permission to show it to you yet. If you only look at net income, you are missing this constant float of “free” capital. It is a business model that essentially funds its own growth through its customers’ wallets.

When you piece it all together – the accounting dynamics, the non–cash amortization, and the upfront cash collection – you realize the P/E ratio isn’t just suboptimal. It’s a distraction.

In the excerpt above, I detail the unique challenges of valuing a company like Topicus, which, despite its growth and strategic acquisitions, may appear overvalued when looked at through the lens of the P/E ratio. As highlighted in the case of Topicus, the P/E ratio doesn’t capture important aspects like the impact of non-cash revaluation losses, deferred revenue, non-controlling interests, or the nuances of the company’s capital allocation strategy.

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Part 3: But What if I Change the Denominator?

After discussing the limitations of the P/E ratio, one might naturally wonder whether using an alternative denominator – like “Free Cash Flow (FCF)” or if you want to out yourself as a Buffett disciple, “Owner Earnings” – might offer a more accurate picture of a company’s value.

The logic behind this is simple: while the P/E ratio is based on net income, there are several other financial metrics that can give a clearer picture of how much cash – real profits – a company is actually generating, which is ultimately what matters for long-term growth and shareholder returns.

However, while these alternative denominators may address some of the flaws of the P/E ratio, they also come with their own set of challenges and require further adjustments. Let’s dive into how these alternatives compare and whether they solve the underlying issues we’ve discussed.

Free Cash Flow is often seen as a more reliable measure of a company’s financial health compared to net income. It represents the cash a company generates after accounting for capital expenditures (CapEx), and it’s a key indicator of whether a company has enough resources to reinvest in the business, pay dividends, or reduce debt (capital allocation decisions). Because free cash flow focuses on cash generation rather than accounting earnings, it provides a clearer view of a company’s ability to create value over time.

Another metric that can provide a more accurate valuation snapshot may be Owner Earnings. This concept, popularized by Warren Buffett, refers to the “true” cash earnings available to shareholders, after accounting for necessary capital expenditures, working capital changes, and non-recurring items. Unlike the P/E ratio, which doesn’t adjust for these factors, Owner Earnings gives a more nuanced view of a company’s ability to generate sustainable returns.

The major advantage of both Free Cash Flow and Owner Earnings over the P/E ratio is that they focus on actual cash generation, which is what really drives the long-term value of a company. A company may have strong earnings on paper, but if those earnings don’t translate into cash flow – whether due to high capital expenditures, accounting adjustments, or other factors – then the P/E ratio can be misleading.

However, even these alternative denominators have their limitations, and there are nuances that need to be taken into account.

While Free Cash Flow and Owner Earnings can offer a more accurate view of a company’s financial health, they are still subject to the same factors (e.g. ROIIC and reinvestment rates) and accounting complexities we discussed earlier – all influencing the actual “fair” multiple.

To fully account for the real value generated by a business, many investors, including myself, use a more adjusted version of Owner Earnings, which strips away non-recurring and non-cash items, such as stock-based compensation, capitalized R&D, and interest payments. Here’s an example of how to adjust for these items:

Owner Earnings = Operating Cash Flow (OCF) – Capital Expenditures (CapEx)
+/- Adjustments:

• Subtract Stock-Based Compensation (SBC)

• Adjust for Lease Principal Payments (IFRS 16)

• Account for Capitalized R&D

• Subtract Interest Paid if reported in financing activities
  +/- Normalized Working Capital Adjustments (e.g., changes in accounts receivable or payable)
  +/- One-Off Tax Effects

This adjusted formula provides a clearer picture of a company’s true earnings potential by accounting for these accounting distortions.

No Single Metric is Perfect

Ultimately, while Free Cash Flow and Owner Earnings offer improvements over the P/E ratio, no single metric is perfect. Each has its own set of challenges and limitations and requires critical judgment.

Investors should be wary of relying too heavily on any one metric, whether it’s P/E, Free Cash Flow, or Owner Earnings. A more comprehensive approach involves looking at the entire financial picture, including capital allocation decisions, reinvestment rates, competitive advantages, debt, and the company’s exposure to risks (such as tail risk or disruptions in its industry).

By combining multiple metrics and adjusting for key accounting complexities, investors can gain a better understanding of a company’s true value – one that goes beyond surface-level earnings or multiples. This holistic approach allows investors to make more informed decisions and avoid the pitfalls that come with over-reliance on a single valuation metric.

Part 4: The Other Half of the Equation: The “P” in the P/E Ratio

While much of our discussion around valuation ratios like the P/E ratio so far focused on the denominator (earnings), it’s essential to also consider the numerator, which, in this case, is the market capitalization. The “P” in P/E can be just as flawed as the denominator and, when misunderstood, can lead to major miscalculations in valuation.

One of the most significant flaws in the “P” is that it does not account for net cash or net debt. This is crucial because a company with a strong cash position or minimal debt may deserve a lower multiple due to its ability to weather economic downturns, invest in growth, or return capital to shareholders. Conversely, a company with heavy debt might deserve a higher multiple due to the additional risks associated with its financial structure. And of course, if you’d acquire the entire company, you’d acquire the debt too!

For example, imagine a growing company that trades at a P/E ratio of 20, which, on the surface, seems reasonable. However, upon closer inspection, you find that the company holds a significant portion of its market capitalization in cash. When you adjust for the cash on hand, the company’s effective P/E ratio could drop significantly, making it actually more attractive than the headline P/E figure would suggest.

On the flip side, a company that’s highly leveraged could have a deceptively low P/E ratio.

Cash on the Balance Sheet & Value Traps?

But here’s where even more nuance comes in: cash on the balance sheet can sometimes become a value trap. If a company is sitting on a large cash hoard, yet management has no clear plans to deploy that cash productively – yes, I’m looking at you Japanese management teams ... – then that cash may not be worth much to shareholders, and valuing the business on its Enterprise Value instead of its market cap may turn into yet another pitfall.

In these cases, even if the company’s “adjusted P/E ratio” looks much more attractive because of its large cash reserves, the market could still be mispricing the stock. Cash sitting idly on the balance sheet for decades does not generate returns for shareholders.

The same principle applies when looking at debt. A company that takes on a significant amount of debt might appear to be undervalued based on its P/E ratio, but the financial risk tied to that debt can often outweigh the apparent value. Debt can be a double-edged sword—it might fuel growth in the short term, but if it isn’t managed properly, it could lead to financial strain, especially in periods of economic volatility.

Conclusion: Rethinking the P/E Ratio and Embracing a Comprehensive Approach to Valuation

The Price-to-Earnings (P/E) ratio is undoubtedly one of the most popular tools in an investor’s toolkit. It’s simple, it’s familiar, it’s accessible to beginners, and it provides a quick snapshot of how “expensive” or “cheap” a stock might be.

But as we’ve seen, it really DOES NOT.

This seemingly straightforward metric comes with significant flaws that can easily mislead investors, especially when used in isolation.

The P/E ratio fails to account for key factors like a company’s reinvestment rate, the efficiency of its capital allocation, and the true quality of its earnings. It doesn’t capture the durability of a company’s competitive advantages, its exposure to tail risks, or its ability to generate sustainable growth over the long term. In some cases, like with Topicus, the P/E ratio fails miserably to reflect business-specific accounting practices.

Additionally, while adjusting the denominator to metrics like Free Cash Flow or Owner Earnings can provide a clearer view of a company’s cash-generating ability, these metrics come with their own set of challenges. Accounting complexities, like stock-based compensation, capitalized R&D, and working capital manipulation, still need to be carefully accounted for to avoid overestimating a company’s true financial earnings power.

But perhaps just as importantly, the numerator, the “P” in the P/E ratio, introduces its own set of distortions. The market cap fails to account for the company’s net cash or net debt, which can significantly affect the multiple.

So, what’s the takeaway?

The P/E ratio is just the starting point, not the be-all and end-all of stock valuation. Investors need to go beyond the numbers, adopting a more holistic approach to valuation that includes an in-depth analysis of a company’s growth potential, capital allocation strategy, risk factors, and balance sheet dynamics. Understanding a company’s true earnings power, factoring in net cash or net debt, and adjusting for accounting complexities is crucial for getting an accurate picture of its long-term value.

As investors, we must be willing to embrace a deeper, more nuanced understanding of the businesses we invest in. This is a competitive field and if you want to generate alpha, you better work hard on improving your valuation skills. Relying on a single multiple like the P/E ratio may seem convenient, but it’s a shortcut that often leads to more harm than good (i.e. underperformance). A more comprehensive approach will help you make better-informed decisions and uncover opportunities that others might overlook.

In the end, it’s not about rejecting the P/E ratio entirely—it’s about recognizing its limitations and using it as part of a broader toolkit. In a complex and dynamic market, that’s the only way to truly uncover the value of a business and make intelligent, long-term investment decisions.

I understood why traders used charts. I didn’t see why a long-term investor should care. But as discussed in the prior two parts, I noticed that prices increasingly started to move for reasons that had little to do with fundamentals.

Parts 1 and 2 of this series were about diagnosing that shift. Market structure changed. The investor base changed. Perception became a dominant force in price formation. If that diagnosis is even roughly correct, then ignoring price entirely becomes an odd choice. Price is where all of those forces meet. Flows, positioning, fear, enthusiasm, constraints – they all express themselves through price before they show up cleanly anywhere else.

Consider what Druckenmiller said in this 2009 interview (~07:35 mark in the video below):

"If all the news is great and the stock's not acting well, get out...which is a pretty simple thing that for some reason analysts don't know."

What Druckenmiller is basically saying is that if fundamentals and news are strong (or weak) but the stock price isn't rising (or is falling), it signals the market knows something you don't (either negative (hidden risks) or positive). Price action often trumps analysis, as it reflects collective knowledge.

This is the lens through which I think about charts today. Not as predictors. Not as truth. But as an additional layer to consider. Price tells you how the market is reacting to information, not what the information objectively means.

For a fundamental investor, that distinction is crucial. Fundamentals tell you what a business might be worth. Price tells you how the market currently feels about that assessment – and how extreme that feeling has become.

There is also a very practical motivation. As I discussed in Part 1, there are only a few levers available to generate excess returns: security selection, position sizing, and timing. Most fundamental investors spend almost all their time on the first two. Timing is often dismissed as either impossible or irrelevant. In reality, it sits somewhere in between. You don’t need to be good at timing to be successful. But being systematically bad at it is costly.

This part of the series is about avoiding that cost. Not about calling tops and bottoms. Not about trading frequently. And certainly not about replacing business analysis with lines on a chart. It’s about using price behavior to improve decisions at the margin. Slightly better entries. Slightly better exits.

Fewer situations where you buy into accelerating negative perception or sell into the early stages of a positive shift.

Think of technical analysis here as a diagnostic tool. If fundamentals answer the question “what should happen,” and perception analysis helps explain “why the market might be wrong,” then price helps answer “when am I fighting the tape, and when am I aligning with it.” Used this way, charts don’t undermine a fundamental process. They finetune it.

So again, if Parts 1 and 2 were about understanding why mispricings exist and how perception changes, this part is about using quantitative tools to decide when to act – with humility, restraint, and a clear understanding of their limits.

Credits Where Credits Are Due

I also want to acknowledge Tiho Brkan, who taught me most of what I share in this piece. His thinking, practical frameworks, and generosity with knowledge have shaped the way I approach this topic, and many of the ideas here are a direct result of what I learned from him. Any clarity you find in this article is thanks in large part to his influence.

Part 1:

The full story starts here:

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Market structure changed. The investor base changed. Time horizons compressed. Perception became a dominant force in price formation, often overpowering valuation for far longer than many fundamental investors expect or feel comfortable with.

If that diagnosis is broadly correct, it naturally raises the next question. What do you actually do with it?

Read part 1 here:

This second part is my attempt to answer that question on a qualitative level. Before getting into signals, indicators, or charts, I wanted to understand how perception changes can be identified in the first place. What patterns repeat? What kinds of situations reliably distort investor perception? How do you distinguish between a stock that is cheap because something is genuinely broken and one that is cheap because the market is temporarily focused on the wrong thing?

Much of what follows is inspired by how Ricky Sandler articulates and operationalizes these ideas, but filtered through my own process and experience. The goal is not to copy his framework, but to adapt the underlying logic in a way that fits my very fundamentally driven approach.

In the third and final part of the series, I’ll then take the next step and move into quantitative instruments and technical analysis. Not as a separate discipline, and certainly not as a replacement for fundamental work, but as a complementary toolkit.

If this second part is about sharpening qualitative judgment around perception shifts, the next one will be about using data, signals, and price behavior to support timing decisions around entries, exits, and position management. Think of it as adding a new layer to the same process rather than switching frameworks altogether.

So let’s get started!

This is where it gets interesting!

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I want to start this series with a small confession. Towards the end of last year, when I recorded a podcast with Niklas from , we talked about our biggest lessons from 2025. My answer surprised me a little as I said it out loud.

I told him that I had become much more open to the idea of “(market) timing” – and yes, I deliberately put market in brackets.

What I really meant was timing in a broader sense. Timing entries. Timing exits. Paying closer attention to when I deploy capital and when I step aside, even if the underlying business thesis remains intact. That answer would have felt uncomfortable for me a few years ago, I must say. Today, it feels like I’m more honest with myself and the changes in market structure over recent years that have become more and more obvious ever since COVID.

In this context, I have to refer to Michael Mauboussin, who argues that there are only three ways to express skill in investing:

• security selection,

• position sizing and

• timing.

I wrote about this framework in my Half-Year Investor Letter and specifically focused on position sizing. You can find the letter here:

Those are the levers. You can generate alpha by being better at picking stocks. You can generate alpha by sizing positions more intelligently. And you can generate alpha by being better at timing – when you enter, when you add, when you reduce, when you exit.

For a long time, I focused almost exclusively on the first two. I don’t regret that. In fact, you can do extremely well just pulling those levers. And I mean extremely well!

But ignoring the third entirely starts to look less like discipline and more like … stubbornness. It’s important to always be open with yourself and biases you inevitably develop over time. Most value investors develop a “value bias” over time and reject any other approach as nonsense.

But especially once you really internalize how compound interest works, how can you ignore a lever that may help you add a few percentage points to your long-term CAGR? Adding one or two percentage points to your long-term return doesn’t sound dramatic. Compounded over decades, it is anything but small. It is massive. It’s life-changing. Will be worth MILLIONS of dollars.

Someone who really pushed me to think about this differently was Tiho Brkan. At some point last year, he told me something that stuck. He said that, for me personally, the incremental return on time spent improving my “timing muscle” was likely far higher than the return on spending even more time refining my fundamental analysis. His point wasn’t that fundamentals don’t matter. Quite the opposite. It was that fundamentals are already where I spend most of my time and where, at the risk of sounding arrogant, I believe I already have a meaningful edge over the average market participant.

Timing, on the other hand, remains underdeveloped in my process. I still carry a strong “value bias.”

So I’ve been thinking about this expanded framework for at least half a year, probably longer, and yet I’ve barely leveraged it in practice. That tells me something. Transitions like this take time. Writing about them is part of the work. That’s what I attempt to do in this multi-part series. And that’s what I actually did last year when I wrote about market timing, and in hindsight, I believe it was one of the better pieces I published in 2025.

In a way, this series is a continuation of that line of thinking, but with a much sharper focus on timing entries and exits in individual stocks rather than trying to time the market as a whole or call macro turning points.

Another major influence was an episode of the Value Investing with Legends podcast with guest Ricky Sandler, titled “Investing Through Perception Shifts and Market Cycles,” published on December 19, 2025. Much of what I explore in this first post of the series is inspired by how Sandler articulates the idea that markets have changed and that investor perception has become a far more important driver of prices than many fundamental investors are willing to admit. I’m trying to integrate some of his into a framework that still starts with fundamentals but then adds the timing layer on top. When I later get into the more quantitative and technical instruments I use to express this approach, voices like Tiho’s, Jessica Ablamsky, and Mike, with whom I recorded the Wise episode, will play a much stronger role.

And with that out of the way, let’s get into the main sections of this post.

Before we dive back in, a quick note…

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Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also, double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

When Fundamentals Stopped Being Enough on Their Own

For a long time, my implicit mental model of markets was simple. If I understood a business better than most, normalized earnings sensibly, and bought at a price that embedded too pessimistic assumptions, the gap between price and value would eventually close. Maybe not next quarter. Maybe not next year. But eventually. Time, in that framework, was a tailwind. And if you add incremental earnings power growth on top of this, you end up with a sweet, market-beating return.

That belief was not naïve. It reflected how markets actually worked for decades. Prices were largely set by investors doing bottom-up work, comparing businesses against each other, arguing about margins, returns on capital, growth durability. In that world, mispricings tended to correct because the investor base itself was doing the work required to correct them.

What has changed is not the relevance of fundamentals. It’s the reliability of that closing mechanism.

That insight is not unique to Ricky Sandler. It has been voiced, far more bluntly, by David Einhorn, who has spent the last few years openly questioning whether traditional value investing still works at all. In a widely discussed Bloomberg interview, Einhorn described what he called the wreckage of professional value investing. He spoke of “serious changes to the market structure” and argued that “most value investors have been put out of business.”

Statements like these understandably led many investors to conclude that the style itself might be dead. After a long stretch of disappointing performance, it’s not hard to see why both managers and clients have been tempted to move on.

But Einhorn’s argument is more nuanced than the headline quotes suggest. In fact, he immediately follows that bleak assessment with a statement that should make any fundamental investor stop and think. He says,

“the other side of that is nobody knows what anything is worth, so there are an enormous number of companies that are dramatically misvalued.”

That sentence captures the tension at the heart of modern value investing. Fewer people are doing valuation work. Fewer prices are anchored to intrinsic value. On the surface, that sounds like an existential threat to our discpline. Look a layer deeper, and it may be the opposite.

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I wrote a long thread on X unpacking Einhorn’s comments in more detail back in 2023, including excerpts from the Bloomberg interview and some of the implications I drew from it [read it here]. What struck me most while working through his argument was that Einhorn is not really saying valuation is useless. He is saying that valuation has lost its coordinating power. Investors who still value businesses based on discounted future cash flows are increasingly “playing a different game,” as he puts it, than the majority of market participants.

At its core, the logic is straightforward. Stock markets are auction-driven. Prices are set where supply meets demand.

If the marginal buyer in that auction does not care about intrinsic value, is unaware of the concept – or he/she is aware but doesn’t know it want to use it – then prices will not reliably reflect intrinsic value. It’s a reflection of market participants’ indifference. And indifference to price has become much more common.

One of the main forces behind this shift is the rise of passive investing. Over the past decade, trillions of dollars have moved from active strategies into index products. Passive vehicles buy because a stock is in the index, not because it is cheap. They sell because of rebalancing, not because fundamentals deteriorated.

Einhorn also points to the growing dominance of sell-side analysts on conference calls and the increasing influence of quantitative strategies that trade based on factors like momentum, volatility, liquidity, or macro data rather than business value. Taken together, these forces help explain why prices can drift far from any reasonable estimate of intrinsic worth and stay there.

This is where the dilemma for fundamentally focused investors becomes acute. If fewer participants care about value, mispricings should, in theory, become larger and more frequent. That’s the bullish interpretation.

But if fewer participants care about value, the mechanism that used to close those mispricings weakens. Valuation gaps no longer close simply because the gap exists. And that’s a problem for value investors.

Einhorn’s own response to this environment is telling. He has said that he is now much more focused on earning “a return based on what the companies are able to pay us.”

That shift forces you to think more carefully about the sources of equity returns.

• Growth in earnings is one.

• Changes in valuation multiples are another.

  “What we are shooting for is earning outsized returns. So I want these two levers. I want the compounding of the business and the re-rating of the stock.“ - Ricky Sandler

• But there is a third source that becomes more important when re-rating is unreliable: distributions to shareholders. Dividends. Buybacks. Any form of capital allocation that converts intrinsic value into realized returns without requiring the market to change its mind.

This does not mean abandoning valuation. If anything, it raises the bar. When “nobody knows what anything is worth,” capital allocation skill becomes decisive. Buybacks only create value if shares are repurchased below intrinsic value. Dividends only compensate for stagnant prices if the underlying business can sustain them. In a market where perception may never shift, you need businesses that can force value realization themselves.

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Illustrating Einhorn’s Take With An Example:

A simple numerical example helps make this new approach more tangible. Imagine a company trading at $11 per share, generating $1 of free cash flow per share. That’s an 11× free cash flow multiple. Assume the business grows free cash flow at 10% annually over the next three years. If, over the same period, the market decides to revalue the stock from 11× to 14× free cash flow, the result is a very attractive outcome. Your total return would approach 70%, or roughly 19% compounded annually. This is the classic value investing playbook. Earnings grow, the multiple expands, and fundamentals and perception move in sync.

Now remove one assumption. Keep the growth. Remove the re-rating. If the multiple stays flat despite the improvement in free cash flow, your return suddenly looks very different. You’re left with something close to the business growth rate itself. That may still be acceptable, but it is a far cry from what many investors implicitly expect when they underwrite a “cheap stock with growth.”

Now the multiple might also decline if the market doesn’t care about fundamentals anymore. What if the multiple declines from 11x to 8x? Well, all of a sudden you end up with pretty terrible returns despite buying a growing business at a cheap price.

This is where the third source of return becomes decisive. Suppose management recognizes that the stock is undervalued at 8x FCF and decides to use all available free cash flow to repurchase shares in the open market. As the share count shrinks, each remaining share represents a larger ownership stake in the business. Even without any multiple expansion, the math starts to work in your favor. In this scenario, buybacks alone can dramatically lift the compounded return, turning a middling outcome into a very strong one.

Comüare the company not buying back any shares (a 10% share price CAGR) …

… to the one that does so aggressively (a 25x share price CAGR assuming the multiple remains constant):

The same logic applies to dividends. If the company distributes most or all of its free cash flow to shareholders, the return no longer depends on the market waking up. You get paid while you wait (a 22.5% pre-tax return in the example below).

Of course, this cuts both ways. Buybacks only create value if shares are repurchased below intrinsic value. Dividends only help if the underlying business can sustain them without eroding long-term earning power. Capital allocation is not a footnote in this framework. It’s central.

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Now, this is only theoretical so far, let’s look at a real-world company I referred to back in 2023: One of the most frustrating examples I’ve encountered personally was my past investment in Gravity. At one point, the stock traded at roughly one year’s worth of operating income. By almost any valuation framework, it was absurdly cheap. And yet, management refused to buy back shares or return capital in any meaningful way. Cash piled up on the balance sheet. Value remained trapped. In a market where perception alone might never shift, that refusal turned what looked like a no-brainer into an uninvestable situation.

Interestingly, today, almost three years after writing the thread, Gravity’s stock still hasn’t gone anywhere …

… despite improving fundamentals (revenue and profits up):

The stock still trades at a ridiculous valuation. It’s a textbook example of both Einhon and Sandler’s point! One of the most dangerous assumptions in fundamental investing is that cheapness is self-correcting. That assumption used to be reasonable. It’s much less so today.

“Beginning to realize that the gap in value, the time it would take, if you didn't understand why a stock was mispriced and didn't understand what could change that misperception, you could have something that stayed cheap for a very, very long time.“ - Ricky Sandler

The cost of that is not just opportunity cost. It’s also psychological. Extended periods of underperformance test conviction, capital, and client patience.

This is the uncomfortable conclusion you’re forced to confront once you take Einhorn’s argument seriously. If valuation gaps no longer close reliably, you cannot rely on re-rating as a default outcome. You either need a credible path for perception to change, or you need businesses that can deliver returns without asking the market for permission. In today’s market structure, ignoring that distinction is no longer a benign oversight.

“Surely there is some way to not buy as much into a falling knife because that has been the majority of what I did this year, buying improving business fundamentals while the stock keeps declining.“ – Niklas during our recent podcast episode

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

The Six “P”s of Market Structure Changes

Back to Ricky Sandler. He describes this shift very explicitly as well when he reflects on how his own framework evolved after the global financial crisis. He notes that early in his career,

“it was enough to have the right business at the right price and things took care of themselves because most market participants were doing bottoms up research.”

That assumption no longer holds. He goes on to explain that as markets shifted toward passive flows, thematic investors, and increasingly short-term decision-making, “the dislocations from value were persisting for longer and for wider amounts of time.”

A helpful framework for thinking about modern market structure is a simple one: asking what actually drives prices in the short to medium term. Ricky Sandler spends a meaningful part of the conversation explaining why his framework had to evolve, and much of what he describes maps cleanly onto what I think of as the six “P”s of today’s market.

None of these forces are new in isolation. What’s new is their scale, their interaction, and their growing dominance over traditional valuation-driven decision-making.

• The first and most obvious force is passives. We already highlighted this. Index funds and ETFs allocate capital mechanically, based on index weights rather than business fundamentals. Sandler describes how markets shifted away from being dominated by bottom-up investors toward ones “driven by passive flows” and thematic capital (see chart below to back this up with numbers). Indices don’t care about normalized earnings or long-term cash flows. Yet they represent an ever-growing share of daily trading volume and ownership. That alone weakens the link between price and intrinsic value.

  

• Closely related, but more subtle, are “pod shops” – multi-manager platforms and short-horizon trading desks operating under tight risk constraints. The Motley Fool defines those as “[…] a type of investment firm that employs multiple independent teams (or pods) to manage capital. Each team has a specific mandate, style, and edge, and they operate relatively autonomously within the overall structure of the pod shop.“ These investors are often evaluated over weeks or months, not years. Sandler makes this point very explicitly when he talks about “a whole cadre of investors today who are investing on a three-week, a three-month time frame. And what they’re doing has nothing to do with the business and everything to do with the stock and what’s going to make the stock work.” So what matters in a world of “pods” is not whether a business will compound over a decade, but whether a stock will work over the next few reporting cycles. This short-termism amplifies volatility around earnings, guidance changes, and narratives, and it compresses the time window over which fundamentals are allowed to matter.

• Then there is positioning, particularly on the short side. Stocks don’t trade in a vacuum. They trade with an existing shareholder base, embedded expectations, and often crowded exposures. When positioning becomes one-sided, price movements can have very little to do with new information. Sandler alludes to this when he talks about stocks screening poorly in quant baskets, suffering from negative estimate revisions, or being excluded from certain investor universes. Once a stock becomes “unownable” for a segment of the market, selling pressure can persist well beyond what fundamentals would justify. Conversely, when positioning unwinds, perception can flip quickly, sometimes without any real change in the underlying business. InPost came to my mind as a prime example. Apparently, 20% of the free float was borrowed by short sellers despite improving fundamentals of the company and a share price that was down around 50% in a little over a year (before the take-private offer was made public earlier this year).

  

• Processors – algorithms and systematic strategies – are another major force. These strategies don’t interpret businesses. They process data. Factors like momentum, volatility, liquidity, revisions, or correlations drive decisions. Sandler notes that momentum, defined by six- and twelve-month returns, is “one of the most powerful signals over the long term.” Whether that makes intuitive sense is almost beside the point. Enough capital responds to these signals that they become self-reinforcing. A stock with negative momentum attracts selling because it has negative momentum. That selling sustains the momentum. Fundamentals may eventually break the loop, but not always quickly, and not always cleanly.

• People – retail investors – form the fifth P. Retail participation has surged, aided by frictionless trading, social platforms, and constant information flow. Retail flows are often narrative-driven rather than valuation-driven. That doesn’t make them irrational, but it does mean they respond to different stimuli. Stories, themes, sentiment shifts, and social validation can all move prices in ways that are difficult to reconcile with discounted cash flow models. In certain stocks, especially smaller or higher-volatility names, retail participation can meaningfully influence price behavior, at least for extended periods (I referred to Palantir and Tesla as prime examples above).

  

• The sixth P, which Sandler himself emphasizes, is perspective – his term for the narrative surrounding a stock. This is where everything else converges. He repeatedly comes back to the idea that stocks don’t rerate because someone thinks they should. They rerate because other investors who believe it’s only worth 12 times today change their mind. Perspective is what has to change for that to happen. Narratives about growth, durability, cyclicality, disruption, or obsolescence shape how investors interpret the same set of facts. And because today’s market is dominated by participants who rely on heuristics, themes, and signals, narratives can matter as much as numbers, sometimes more, at least in the short to medium term.

  “It’s not going to 18 times because you think it’s worth 18 times. It’s going to go to 18 times because other investors who believe it’s only worth 12 times today changed their mind and they now believe it’s worth 18 times because they think the future beyond that point warrants it or the company’s characteristics warrant it. Beginning to realize that the gap in value, the time it would take, if you didn’t understand why a stock was mispriced and didn’t understand what could change that misperception, you could have something that stayed cheap for a very, very long time.”

Taken together, these six forces explain why prices often behave in ways that feel disconnected from fundamentals. They also explain why valuation gaps can persist, widen, or suddenly close without any obvious catalyst. This is the environment in which fundamental investors now operate.

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Returns Come from Multiple Engines

In the segment focused on Einhorn’s recent takes, we highlighted the three sources of return: earnings growth, changes in valuation multiples, and direct distributions.

Most fundamental investors already focus on the first lever. We spend enormous amounts of time thinking about durability, reinvestment opportunities, competitive advantage. That work remains essential.

Einhorn flagged how you should place a stronger emphasis on the “distribution source” by betting on management teams capable of identifying mispricings and exploiting them with the help of their capital allocation toolkit.

Sandler placed a stronger emphasis on the re-rating lever, driven by perception change. Sandler’s sweet spot, as he describes it, is owning stocks for two to four years and capturing “multiple years of compounding along with a significant re-rating.” That time frame is long enough for fundamentals to matter, but short enough that perception has to shift for returns to be realized.

But how do you spot changes in perception leading to changes in valuation multiples? Well, this is what we are going to cover in the second (qualitative instruments) and third part (quantitative instruments) of this series. Timing becomes everything. And so you need to work on your “timing skills!”

Bottom Line

This first part is deliberately theoretical. Before talking about tools, signals, or techniques, it’s necessary to accept that the rules of the game have changed. Not completely. Not irreversibly. But enough that ignoring those changes becomes a liability.

Fundamental analysis still tells you what should happen over the very, very long term. Market structure and perception analysis help you understand when and why it might actually happen.

The rest of this series builds on that distinction.

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I’m of course biased – I love the show and I love learning from people who treat investing as a lifelong puzzle rather than a quarterly performance sport. But even adjusting for bias, this was an extraordinary conversation. One of the best of the year.

Maybe the best of 2025 on investing.

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Ellenbogen didn’t start in finance. He studied organic chemistry, history, and technology, and spent years working in politics. That unusual path ended up giving him an advantage later, because he learned to study companies like ecosystems and leadership like a repeatable edge.

In this conversation, he touches on themes that matter more now than ever: second-time founders, hidden physical moats, the interplay between AI and real-world infrastructure, how to build positions when conviction grows, and the brutal opportunity cost of misallocating attention.

I left the episode with pages of notes. I picked five core ideas from the conversation that felt most valuable to me. This post is my attempt to actually internalize them. Writing them down forces me to slow down, think harder, and remember better. It’s the same reason Ellenbogen built a memo culture into his firm. You can hear something smart. Or you can absorb it. The second option takes longer. But it compounds too.

The sections that follow are written for the same kind of reader I am – someone who wants frameworks that can be applied to improve one’s process, and lessons that stick around during calm and turbulent market times. Let’s get into the first one.

Insight #1: Power Law Distributions & Hunting for the Real Outliers

One of the most clarifying moments was how direct Ellenbogen is about the shape of outcomes in public markets. While managing T. Rowe Price’s New Horizons Fund, he went into the archives to decode half a century of performance. The conclusion hit hard: only 20 stocks explained most of the fund’s returns. He later distilled that realization into the brutal arithmetic of capitalism itself: over a rolling 10-year period, roughly 40 companies compound at 20% annually or better, crossing the 6x threshold. That’s about 1% of the market.

This insight reminded me of the now-famous study by Hendrik Bessembinder, often misquoted as evidence that equities fail over time. What the research really showed is something more precise and far more interesting: over very long horizons, the majority of individual stocks did not outperform 1-month T-Bills. Roughly 58% of U.S. stocks underperformed cash-like government bills over their lifetime. Yet – and this is the part that matters – a tiny fraction of extreme winners created essentially all of the net wealth generated by the equity market since 1926. Around 4% of stocks explained the entire cumulative gain, with the top 1% contributing a disproportionate share of total compounding.

But back to Henry Ellenbogen. His Walmart story sharpened the point further: Walmart went public with only 50 stores, tiny at the time, iterating under a founder who thought like an owner. “And obviously Walmart became Walmart.“ But it was sold early by the fund. The lesson Ellenbogen carried forward wasn’t just that Walmart worked. It was that trimming or exiting a true outlier can mathematically erase dozens of correct but smaller decisions. He summed it up with the kind of clarity that sticks: “The math was one bad decision, or maybe you had to make that decision every day because the public markets are open every day, […] [which] wiped out all these other good decisions.”

Ellenbogen then talked about where to find these outliers. Most giants don’t start as giants. They become them. “About 80% of those companies start their compounding journey as small cap companies.”

In sum, if markets are this skewed, the real risk isn’t being wrong often. It’s being wrong on the few names that matter. Over-diversification into average businesses, trimming winners because of macro fears, or letting short-cycle incentives dictate your time horizon …

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Insight #2: Act 2 Entrepreneurs – Pattern Recognition in People

Henry’s concept of “Act 2 teams” is arguably one of the most practical mental shortcuts shared during this episode. The idea is almost deceptively simple: founders who have built and scaled a company before start their next venture with sharper first principles, cleaner incentives, and scar tissue that actually helps them execute better and faster.

He illustrates it with Workday. The founders had already pioneered HR systems of record at PeopleSoft before Oracle’s takeover cut their original vision short. When cloud computing emerged, they attacked the same problem again – but this time with total clarity on the product, the edge cases, the go-to-market, and how to align the whole organization.

One quote that captures this advantage perfectly is: “and there's a huge advantage when you go do it again, you are essentially solving the same problem but with total clarity at the beginning.“ Another line that captures the idea is: “If you’ve been one before, you have a higher probability of being one again.”

Max Levchin, one of the co-founders of PayPal, is another recurring example for him. Ellenbogen had backed Max in his previous company, Slide, and later invested in Affirm. He calls out the specific attributes that matter most in these entrepreneurs:

• communication clarity,

• recruiting strength, and

• resilience under pressure.

For his fund, networking is a critical advantage here, too. Oftentimes, Ellenbogen and his team benefit from “compounding relationships” as he explains in this passage:

“If Durable does anywhere from five to 10 new investments a year, a lot of times it actually is with Act 2 entrepreneurs. And then if we're lucky, and this happens because of compounding relationships for having done this for 20 years, a lot of times it's with people that actually we were investors in their previous act. And I think that's also great, because they know us and we know them, we know each other's strengths and we know exactly how we can help each other.“

Henry Ellenbogen’s point is that betting on successful past founders gives you a probabilistic edge – the base rates go up essentially. A founder who has seen scale once and survived the stress that comes with building in public is a fundamentally different “asset class” than someone learning the ropes for the first time.

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Insight #3: Physical World Moats Are Underrated

Ellenbogen lights up when he talks about the physical world. He loves moats rooted in physical infrastructure, the kind that require land, logistics, permits, capital, sequencing, and a culture that actually works once the assets are in place.

Strictly speaking, he doesn’t just like companies with physical moats. He likes companies where physical-world assets and the “tech stack” work together, feeding off each other. The physical assets create the initial constraints and technology is the lever that bends those constraints into a widening gap, a widening moat. He said, “ideally what we go do is we go find those already advantaged companies and then they leverage this [note: a new innovative force] to go from good to great.”

He also emphasizes location quality as part of unit economics, not just operations: “If you put your real estate in the wrong place, then your cost of transport’s more expensive.” In low-margin industries, small cost deltas compound into structural share shifts. And the companies that figured this out early – like Amazon acquiring Kiva and embedding robotics into their network – created advantages that translated from digital signals into physical dominance.

Understanding that technology can amplify physical distribution moats was a key insight for me. So if you’re building a mental database of businesses, don’t skip the “ugly” ones, the capital-heavy ones.

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Insight #4: Digital Kaizen vs Physical Kaizen

Digital Kaizen and Physical Kaizen are often presented like opposing worlds, but Ellenbogen sees them as sequential, not rival, forces. When Patrick O’Shaughnessy floated the idea that “if [physical Kaizen and digital Kaizen] were to have a kid, it might be robotics And so I'm really curious how you approach the potential, with an unknown timeline, that we might get a second wave of […] Kaizen over the next 40 years,” Ellenbogen expanded on this idea.

“Kaizen (Japanese: 改善; “improvement”) is a Japanese concept in business studies which asserts that significant positive results may be achieved due the cumulative effect of many, often small (and even trivial), improvements to all aspects of a company’s operations. Kaizen is put into action by continuously improving every facet of a company’s production and requires the participation of all employees from the CEO to assembly line workers. Kaizen also applies to processes, such as purchasing and logistics, that cross organizational boundaries into the supply chain.“ - Wikipedia

His answer began with caution: “our views here are very early and probably deeply wrong,” but quickly turned into a mental model worth paying attention to.

He first describes how Kaizen reshaped physical industries for 40 years – driving working capital efficiency, factory throughput, and supply-chain radius advantages.

Now, AI represents a similar shift for knowledge work. Digital Kaizen can create extraordinary speed gains. He uses Duolingo’s CEO Luis von Ahn to illustrate that point: the ability to build a new product, Duolingo’s chess module – which, as Duolingo reported in its latest quarterly earnings, has become the “fastest-growing subject” in the company’s history –, in nine months with a fraction of the people it would have required before. Speed like that matters, but speed alone is not creating defensibility & durability.

Historical base rates are useful, but not fully predictive here. The curve of improvement is steepening.

Physical Kaizen, as discussed in insight #3 to some extent, creates defensibility that can persist for decades. Amazon is his canonical example. The company didn’t just get better at software – it used software to push itself onto a deflationary logistics curve, then reinvested the advantage into physical infrastructure. In his words: “they took that 3-5% cost advantage of getting that box to you and their ability to put more than one item in the box, and they used that economic advantage to then go build fulfillment centers that are physical to reinvest into capital and infrastructure that allowed them to go down that 3% to 5% cost curve for 20 years.” Once the infrastructure was in place, only companies that still had real scale and trusted customer relationships – Walmart and Costco – could keep up when the curve became obvious to everyone else.

And we may be seeing similar dynamics playing out when it comes to integrating and leveraging robotics. Here’s what Ellenbogen had to say on this:

“… what we’re starting to get our heads around at Durable, which is in many areas robotics is at parity. Now there’s a lot of data that says it’s lower cost in certain cases. And the use cases are about to go up, and the cost on the existing use cases probably don’t go down at 3-5% at this point in the curve because of the scale brought in, the human capital brought in, the IP bought on. And the fact that you’re gonna be able to use these general purpose LLMs to power it, it probably goes down at more like 15 to 20%, but maybe it’s Moore’s law and it goes down even faster. Well then we wake up in five years and the people who put themselves on one cost curve, if they compete against the people who put themselves on the other cost curve, those could be power law businesses.

What we have thought really hard about is who’s gonna go benefit from this curve, where their competition—even if they woke up tomorrow, even if they put the same amount of money into this problem, even if they could hire the same quality of people, which is unlikely. Because they have not invested in the distribution infrastructure or the technological infrastructure to compete on this curve, at minimum, is probably two to three years behind and every day that they wait, they’re probably getting further behind.“

I kept thinking of InPost who may be a modern-day example of the concepts discussed here – they are investing aggressively in their physical-world moat/infrastructure (and have arguably reached a scale, no one will be able to catch up with anytime soon) while leveraging AI in their distribution centers, via their app, etc. (disclaimer: I own InPost shares; find my deep dive below).

The real insight for me is this: growth curves in companies aren’t only about how many customers they gain. Some curves describe how a business transforms itself – how it works, how fast it learns, how fast it lowers cost, how fast it scales output without scaling people. And AI represents an entirely new lever here. When a company absorbs AI or automation early, that may be the beginning of a new S-curve too – not in users, but in operational efficiency. By betting on companies that are nimble enough to adopt AI early, you’re underwriting a shift in how work gets done inside the business, which eventually decides who pulls away from the pack, and who gets left behind.

Insight #5: The Case for Public Markets

Ellenbogen does make a strong argument for companies becoming publicly traded. What resonated most is that he views the public markets as a mechanism that enforces feedback, expands scenarios management teams consider, and enables sharper internal alignment, decision-making, and capital allocation – especially during moments of transformation.

He acknowledges that staying private can be thoughtful for certain companies: “Maybe Elon is correct and SpaceX never has to go public. That’s really only happened in the last five years. Some people correctly pointed out this might be an incredible path for certain companies. And by the way, I think there’s some truth in it.”

But he also stresses that this is a new phenomenon, and that we’ll eventually know which model wins: “Here’s the good news about life. We’re gonna actually run an experiment. We’re gonna actually know the answer. I’m not saying that’s wrong.”

Still, his belief is clear: building durable compounders through public markets is a proven path, particularly when leadership knows how to use feedback constructively: “I believe the path to building a great company… through the public markets is proven.”

One of his most memorable points comes from the Netflix example. Reed Hastings’ shift from DVDs to streaming looked, as Ellenbogen puts it, “a little messy,” but the mess forced a conversation that made Netflix stronger. When Henry Ellenbogen called Reed on a Saturday to walk him through the financial implications of moving from a variable-cost model (renting DVDs from studios on usage) to a fixed-cost model (cutting large upfront checks for content and original programming), Reed initially pushed back: “Henry, what are you even talking about?” But crucially, he acknowledged the gap in his own underwriting, and invited the process battle instead of dodging it: “I have not thought about this as much as I should. Let’s talk tomorrow… You go through your scenario, we’ll go through ours and I can learn.”

That exchange is the essence of what Ellenbogen values about public markets. The stock drop from $280 to $70 didn’t invalidate Netflix’s future – it validated the need to expand scenarios and tighten internal alignment while the stakes were still manageable. The public markets acted as a forcing function. Not to punish, but to clarify.

He also reframes the “finance function” in a way that many founders should hear earlier: “And what I always tell people about this is you should think about your CFO's function not as a policeman, but actually as someone who basically sells standards that force you to make sharp decisions [on capital].”

Ellenbogen also emphasizes that public companies don’t need to hit steady-state profitability overnight – but they must show direction. He recalled a conversation he had with Duolingo CEO Luis von Ahn: “You have a very unique humic capital culture, but if you’re gonna communicate to people this strength in the market, it doesn’t mean you need to get to your long-term margin targets at 30%, but you have to show progress towards it.“

The takeaway for me is simple. The public markets are not just a place to raise capital. They are a place that sends signals early, forces disciplined capital allocation, and aligns internal incentives.

Concluding Thoughts

What I maybe admire most about Henry Ellenbogen is that his thinking refuses to sit still. He rigorously studies markets, current trends, new innovative forces, the influence of incentives, and the patterns of “compounding companies” building their moats and legacy.

I love Invest Like The Best; not because every guest is aligned with my very own philosophy, but because some of the most interesting conversations make me revisit my own process, challenge my previous assumptions, and add new mental models and insights.

I picked five ideas from this conversation in this piece that felt most important to me, but there are more ideas in the episode; so I encourage you to listen to the entire episode next:

The sudden rise of Kalshi, capped by its $11 billion valuation and the instant celebrity of Luana Lopes Lara – now the youngest self-made female billionaire –, has dragged the entire category from the fringes of finance into the mainstream.

Polymarket is growing at breakneck speed. Even Robinhood is experimenting with event contracts that sit somewhere between betting slips and micro-futures.

Whether these instruments belong in the realm of investing, speculation, or pure entertainment is already a contentious question. I’m not entirely sure myself. What I am sure about is that their popularity is revealing something far more interesting than the surface narrative of gamified prediction.

Prediction markets offer an easy-to-grasp way to observe how auction-driven systems absorb information. They compress the mechanics of price discovery into a narrow, almost clinical format.

A simple contract that resolves into yes or no by a fixed date forces participants to translate every incoming piece of news into updated probabilities. That constant weighing of information against price is a process you rarely see so clearly in other markets.

What’s fascinating is how often these markets settle near the correct outcome long before expert commentary or media consensus catches up. I’ve seen it repeatedly.

• Election probabilities tightening weeks before pollsters adjusted their models.

  

• Contract prices for central bank decisions shifting hours after a subtle comment from a single policymaker.

  

• Geopolitical risk markets inching upward while traditional coverage still framed the situation as stable.

  

• Even court-case markets have displayed an uncanny ability to interpret legal signals that outsiders dismissed as irrelevant.

It’s uncomfortable to watch these mechanisms work because they confront a deeply held belief (by many) that expertise should outperform the crowd.

Yet whenever money is on the line and incentives are sharp, the crowd often becomes the expert.

The dynamics behind that efficiency aren’t mysterious. They emerge from a mix of incentives, liquidity, swarm intelligence, and relentless feedback loops.

A trader is forced to update their beliefs in real time. They cannot cling to an outdated view without paying for it. Every new piece of information pushes the market to a new equilibrium, even if only slightly, and the aggregate of these micro-adjustments creates a remarkably sensitive forecasting machine – most of the time at least.

If you ever want a reminder of how powerful decentralized information processing can be, simply scroll through the price history of a major prediction contract during a volatile event.

This is where the connection to equity markets becomes unavoidable. Stocks are auction-driven systems too. They aggregate countless views, time horizons, and emotional impulses into a single price. Investors sometimes forget how brutally efficient this mechanism can be. Prediction markets remind me of that efficiency in its purest form. They show how quickly a market can reprice when incentives are tight, information is diffuse, and participants are forced to confront the implications of being wrong.

But they also hint at something else, something easy to ignore when optimism or conviction runs high. If a simple binary contract with a fixed resolution date absorbs information that efficiently, what does that imply for the far more complex ecosystem of equities?

The lesson is not that markets are perfectly efficient all the time. They never are. The lesson is that markets are far more efficient than many investors are comfortable admitting – most of the time at least –, and that any genuine edge must be earned, not assumed.

So let’s discuss that bridge to the world of stocks, what transfers and what doesn’t.

Before we dive back in, a quick note…

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The Bridge to Stock Markets

What Transfers?

The most striking overlap between prediction markets and equities sits in the mechanism of price discovery. Both are auction-driven systems that translate fragmented information, shifting beliefs, and heterogeneous motives into a single number.

That number is always provisional, always conditional, and always ready to move when something new arrives.

You see this clearly in prediction markets because the payoff structure is so rigid. A contract will resolve into one outcome, nothing more. That simplicity exposes the continual tug-of-war beneath the surface. Someone buys because they believe the implied probability is too low. Someone sells because they see it as too high. With enough liquidity, these opposing forces create a sensitive, self-correcting signal.

Equities operate on the same principle, even if the visible noise often obscures the underlying logic. Every tick in a stock price represents an update in the market’s collective understanding of a business and its future.

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One reason this mechanism works so well is that markets function less like machines and more like complex adaptive systems (something I learned from Michael Mauboussin). They don’t rely on a handful of “lead steers” making the right call but operate through the aggregation of many diverse, often flawed decision rules interacting in real time. When enough independent interpretations collide, the system begins to display emergent intelligence at the collective level, even if individual participants are operating with incomplete information.

Investors bring different models, different emotional states, and different time horizons, yet the auction clears regardless.

So when the market moves against you, it’s because someone else has interpreted the available information differently or weighted it more heavily.

Prediction markets teach you humility here. They demonstrate how fast a market can incorporate news, even subtle news, and how unforgiving the process becomes when incentives are sharp.

There’s another important transfer: constant Bayesian updating.

Anyone trading a prediction contract quickly learns that anchoring to an early belief is dangerous. The price forces you to reassess whether you want to stand against the crowd’s interpretation of the latest data. Equity markets operate in exactly this way. They reward flexibility and punish intellectual rigidity. Buy and continuously verify. The investor who refuses to update is, over time, the investor who underperforms.

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What prediction markets reveal, and what Mauboussin’s work reinforces, is that the intelligence of the market doesn’t depend on the intelligence of each participant. Even simple or poorly calibrated decision rules can, when aggregated, create surprisingly efficient outcomes. The structure of the market itself drives much of the accuracy. This is why even traders who misinterpret individual signals can contribute to a system that, taken as a whole, processes information remarkably well.

What Doesn’t Transfer?

For all of the similarities, the differences between prediction markets and equities are equally revealing.

1. A prediction contract resolves into a simple yes or no. A binary decision to be made. Stocks don’t. Stocks stretch out across time and contain multiple possible paths, many of which can be right or wrong depending on who is evaluating them.

2. Then there is the question of time horizon. Prediction markets align all participants around a single endpoint. Everyone plays the same game. Equity markets accommodate countless games that coexist without ever merging. A growth investor, a distressed debt analyst, a quant fund, and a day trader can all be trading the same shares for entirely different reasons. Their beliefs don’t converge neatly. They collide and overlap, creating a more layered and sometimes more chaotic price formation process. But in aggregate, they too often create highly efficient pricing dynamics. But nonetheless, a short-term trader and a long-term investor can still be right at the same time; the short seller can profit from a business’s temporary missteps, while a long-term investor can profit from the exact same business reshaping itself over years (exploiting time arbitrage dynamics).

3. The third major gap is the influence of narrative and momentum. Prediction markets are compressed into a narrow window where the arrival of new information directly forces a repricing. There’s no room for multi-year extrapolation or thematic storytelling or momentum forces. Equity valuations depend heavily on narratives and momentum – at least in the short and medium term. This introduces a layer of path dependency that prediction markets never have to confront.

4. Fourthly, liquidity also behaves differently. Stock prices can be distorted by forced sellers, leverage unwinding, or institutional constraints unrelated to fundamentals. Prediction markets don’t suffer from those pressures to the same degree. They occasionally lack liquidity, but they don’t see the cascading, multiday – multi-month somestimes – liquidation patterns that show up in equities.

5. Another gap that prediction markets don’t capture is the inherent nonlinearity of equity markets. Stocks exhibit fat-tailed return distributions, punctuated by long stretches of calm followed by abrupt dislocations.

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The Lesson?

The implications for investors are far-reaching. Prediction markets reveal how efficient price discovery can be when incentives, information, and liquidity snap tightly together. If such a simple contract can absorb information with that level of precision, you have to assume that equities, with thousands of intelligent participants and billions of dollars at stake, possess a baseline efficiency that is difficult to overcome. That doesn’t mean opportunities don’t exist. It means the window for exploiting them is narrow, unstable, and rarely obvious in real time.

Understanding what doesn’t transfer is equally important. The complexity of equities creates pockets where inefficiencies can survive.

1. Time horizon is one of them.

2. Behavioural resilience is another.

Opportunities often appear when diversity begins to break down. A market with many independent decision rules tends to be stable and efficient. A market where too many investors anchor to the same narrative or react to the same signals becomes fragile. It’s in those moments of reduced diversity that inefficiencies emerge and where disciplined investors can find a genuine edge.

The combined lesson is simple to state yet difficult to internalize. Markets are efficient enough that any durable edge must be earned through genuine insight, selective domain expertise, or an uncommon time horizon.

They are inefficient enough that disciplined investors can still find opportunity. Prediction markets illuminate both truths. They remind you how quickly a market can outrun your conviction, and they underline the importance of knowing exactly where your edge begins and ends.

Credits to Infinite Fund who brought up some of the points discussed here.

I love this chart from the Novel Investor, which illustrates short-term sector performance cyclicality quite well. It highlights how, over shorter time frames, the performance of S&P sectors is all over the place, depending on which year you look at – for instance, in 2016, Healthcare was the worst-performing sector, two years later, it was the best-performing one.

Today, the sectors the masses would rather ignore may very well be the broad energy complex, along with the healthcare sector. When you look at sector weightings, investor surveys, and fund flows, it’s hard not to notice how far out of favor they have fallen. Technology keeps absorbing more and more market cap, while defensive and cyclical sectors shrink quietly in the background, as illustrated by the chart below.

On the flow and positioning side, professional investors have moved from mild indifference to outright avoidance. The energy industry has slipped back into a familiar spot on the market’s emotional spectrum: boredom bordering on disdain.

In fact, relative to the current money supply, oil “is currently near its cheapest levels in history“:

Additionally, oil also looks cheap, measured against gold:

Here’s a Preview of What’s Inside the Paywalled Sections (6,500-word deep dive):

• Why and how hated sectors are fertile hunting grounds

• The one mental model that simplifies commodity investing: How a single framework coupled with common sense cuts through the noise of macro predictions and offers a surprisingly powerful lens on the oil cycle.

• What the global cost curve actually looks like today: A detailed exploration of production economics across shale, offshore, conventional, and high-cost barrels

• The hidden story inside reinvestment and supply data: What capital discipline, service-cost inflation, and shifting marginal economics tell us about the next few years of global supply.

• Why the cycle may tighten long before prices signal it

• A differentiated way to play the cycle without relying on oil prices: An in-depth look at ecosystem businesses with dual-cycle characteristics

• How to think about implementation, risk, and timing

• An appendix with a NotebookLM-generated infographic on the current state of the oil market, a couple of videos I found interesting on the topic of commodity investing (and the opportunity in oil specifically), and a bunch of charts on oil stocks’ recent 3-year performance (categorized by their role in the oil ecosystem).

This is where it gets interesting.

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Everyone agrees it matters – a lot! –, but few investors consistently think in terms of opportunity cost – the silent force that shapes the value accretiveness of every decision a management team makes once new cash hits their balance sheet.

Should they reinvest in the business? Pay down debt? Buy back shares? Raise dividends? On the surface, the options look mechanical, even formulaic. In reality, they require judgment, foresight, sometimes creativity, and sometimes a willingness to do nothing – yes, that’s a capital allocation decision too.

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The market tends to reward simplicity. If a company announces a buyback, investors often celebrate. The optics are clean: fewer shares, higher EPS, a tangible signal that management believes the stock is cheap. But as seductive as that narrative is, it can be misleading. A buyback only creates value when the stock trades below intrinsic value. Otherwise, it’s an implicit transfer of wealth from continuing shareholders to those who sell. It’s capital destruction disguised as shareholder friendliness.

Reinvestments back into the business, on the other hand, are messier. The return earned on this allocation of capital doesn’t show up as neatly in quarterly slides. The payback period can be long, the returns uncertain, the outcomes dependent on execution quality, industry structure, and sometimes macro developments management can hardly control. Yet, often, that’s where businesses strengthen their moats, extend their competitive advantages, and plant the seeds for tomorrow’s cash flows.

As investors, we often fall into the trap of treating capital allocation decisions as binary – buybacks versus growth investments, dividends versus M&A. But what really matters is context. Opportunity cost isn’t a static number on a spreadsheet; it’s a concept, often a moving target, shaped by market conditions, valuation, competitive position, and the set of reinvestment options available at any given time.

When I evaluate management teams, I don’t start by asking whether they’re buying back stock. I ask whether they’re thinking like investors – comparing alternatives, ranking opportunities, and deploying capital where the incremental return per dollar is highest after adjusting for risk and durability. That’s a far higher bar than simply repurchasing shares to signal confidence.

Ashtead Technology is a good example of this nuance. The company’s share price has been under pressure, and it’s tempting to wish for aggressive buybacks.

But when I look at the reinvestment opportunities in front of them – the ability to strengthen their one-stop-shop moat, expand their equipment, deepen customer relationships, I find myself rooting for internal reinvestment instead; despite the poor recent share performance.

Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

The Opportunity Cost Framework

When I think about capital allocation, I think about trade-offs. Every dollar of free cash flow can only be spent once – and every use of that dollar carries an implicit opportunity cost. It’s the return you forgo from the next-best alternative.

In theory, management should always allocate capital to the option with the highest risk-adjusted return.

In practice, this is where things get tricky.

The textbook hierarchy goes something like this: first, reinvest in the core business if incremental returns on incremental invested capital (ROIIC) remain attractive; second, pursue strategic acquisitions at attractive prices that enhance the moat; third, pay down debt if leverage is high (+ the interest reduction is a guaranteed return); fourth, repurchase shares if they trade below intrinsic value; and lastly, distribute dividends when no better options exist.

It’s a tidy list. But reality rarely fits the list.

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Sometimes reinvestment opportunities are limited or cyclical. Sometimes management has asymmetric information about intrinsic value. And sometimes, the best capital allocation decision might simply be to wait.

To make this more tangible, I like to share some simple buyback math – and the illustration below of the fictional company “Metavision Inc.” (credits go to Javier Pérez from Edelweiss Capital).

It shows how a $500 buyback can either create, preserve, or destroy value depending on valuation. When shares are overvalued, each dollar used to repurchase stock actually reduces intrinsic value per share. When shares are fairly valued, intrinsic value per share stays roughly the same. And when shares are undervalued, buybacks become a powerful value creation tool.

In the example, buying stock at half of intrinsic value raises per-share intrinsic value by 33%.

On paper, it looks wonderfully precise. But in practice, it’s anything but. Because what drives the entire calculation – intrinsic value – is itself an estimate.

A range, not a point.

A moving target that depends on assumptions about growth, margins, discount rates, and competitive durability (competitive advantage periods (CAP)). And if you define intrinsic value as the present value of all future cash flows, then every input is a guess.

Of course, you can make the spreadsheet sing, but that doesn’t make it true. Bullsh*t in, bullsh*t out.

That’s what makes buyback analysis only deceptively clean. You can model the effects down to the decimal, yet the precision hides uncertainty. A fair-value repurchase today might turn out to have been overvalued six months later if the underlying business stumbles. And if management misjudges their own intrinsic value – or worse, if their incentives are tied to short-term EPS – buybacks can turn into a recurring wealth transfer from continuing shareholders to departing ones.

Reinvestment, by contrast, is less quantifiable, returns even harder to pinpoint, but often reinvestments are more meaningful for long term earnings durability.

Strengthening a moat, building new capabilities, or entering adjacent markets doesn’t always yield an immediate measurable return. It’s qualitative by nature. A company that compounds intrinsic value at a steady rate over decades is usually one that consistently reinvests in itself, even when buybacks look attractive.

This is where critical judgment enters the room again. Opportunity cost in capital allocation isn’t just a number; it’s THE framework for thinking about trade-offs under uncertainty.

A dollar spent on buybacks at a 15% implied return might look tempting, but if reinvesting that same dollar in product innovation or network expansion expands the competitive advantage period by another five years, increases the defensibility of the business, thereby reducing downside risk and increasing risk-adjusted returns, well then … this investment may be the superior choice.

Depending on your financial goals and circumstances, longevity of return often trumps magnitude.

Or as Pabrai once put it, “return of capital is more important than return on capital.”

The best management teams – the truly great capital allocators – don’t chase the highest short-term yield. They allocate based on the durability, scalability, and strategic optionality of each dollar spent. And that’s something a spreadsheet alone can’t tell you.

That’s why, in the case of Ashtead Technology, I lean toward reinvestment. The company operates in an industry where equipment breadth, scale, and customer trust matter immensely. Strengthening those through organic or inorganic investment likely produces more sustainable returns than simply shrinking the share count. Buying back stock might make near-term optics cleaner and the chart look less painful, but reinvesting builds the foundation for multi-year compounding.

When Buybacks Make Sense – and When They Don’t

There’s a tendency among investors to treat buybacks as a universal good. The logic is intuitive: fewer shares mean higher ownership per share, higher earnings per share, and a signal that management believes the stock is undervalued. But like most things in investing, context matters. As shown, buybacks can be either the most accretive or the most destructive use of capital – and sometimes, they’re neither. They’re just a way to smooth the ride.

Buybacks make sense when two conditions align. First, the stock must trade at a meaningful discount to intrinsic value. Second, the company must lack reinvestment opportunities offering comparable or better risk-adjusted returns. That combination is rare, because truly great businesses often have abundant ways to reinvest capital at high incremental returns.

The companies most capable of compounding capital internally are often the ones least likely to benefit from buybacks.

That doesn’t mean buybacks never have a place. When a company’s share price has fallen below intrinsic value due to temporary pessimism – a cyclical downturn, a misunderstood earnings miss, or market-wide de-risking – repurchases can also serve as a defensive mechanism, providing short- to mid-term stability by absorbing selling pressure, countering elevated short interest, or signaling internal confidence when sentiment has swung too far. Evolution Gaming as a prime example immediately comes to my mind.

In that sense, buybacks can act as a pressure valve, keeping the share price from detaching too far from fundamentals.

Of course, there’s also a governance layer to all this. When a company trades at a depressed valuation and lacks an anchor shareholder, it becomes vulnerable to opportunistic takeovers.

That’s when capital allocation and corporate integrity intersect. Management must resist the temptation to entertain offers that undervalue the business simply because sentiment is weak. In those situations, alignment of interest and clarity of purpose matter even more than precise capital allocation math.

Ultimately, great capital allocators don’t think in rigid categories. They don’t see buybacks, reinvestment, or dividends as right or wrong. They view them as tools – each with a different purpose and optimal use depending on valuation, opportunity set, and time horizon. The best managers flex between them fluidly, guided not by what pleases the market but by what compounds intrinsic value per share over time.

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PS: Here’s a cheat sheet I once created for my mentoring program:

I first came across Timee when Jake Barfield mentioned it in one of his investor letters a few months ago. I found the business intriguing but didn’t act immediately as I was less excited about the current share price. Then, when the stock dropped by almost 40% a few months later, I started digging in properly, using Barfield’s original analysis as the foundation for my own research.

Later, while chatting with Jake in the DMs on X, discussing Timee, the name InPost came up. Both businesses, Timee and InPost, we agreed, show signs of intense network activity – a dynamic that might be an expression of moat strength.

Until that moment, I had never even heard of InPost. But again I decided to be a shameless cloner and within days, I was deep into the filings, building my own thesis.

That sequence – one idea leading to another, sparked by someone else’s insight – perfectly captures the spirit of what I’m about to discuss in this post, but with a modern twist to it!

But let’s go back a few steps… A few years ago, Mohnish Pabrai said something that stuck with me: “I liberally swipe investing ideas from other value investors.” The first time you hear this idea, it may almost sound too simple. Surely great investors are original thinkers, right? They uncover hidden gems no one else sees, build models from scratch, and spend years studying industries others ignore. Right?

But then you realize – many of the greats are, in fact, world-class imitators. They study what works, internalize it, and apply it ruthlessly. Buffett cloned Ben Graham. Munger cloned Fisher. Akre cloned Buffett. And on it goes.

In a field obsessed with novelty, cloning feels like cheating. But the more you think about it, the more obvious it becomes: investing is a game of pattern recognition, not invention. Why waste years reinventing the wheel when you can stand on the shoulders of those who’ve already built a Ferrari?

That’s what this post is about – re-examining what it means to be a modern shameless cloner. Not a lazy copycat, but an intelligent learner. Someone who understands that cloning, done thoughtfully, isn’t about outsourcing your thinking; it’s about compressing decades of experience into months of study.

Here’s what I’ll explore:

• Where the idea of shameless cloning comes from – and why Mohnish Pabrai, Charlie Munger, and Warren Buffett all practiced it long before it had a name.

• Why idea generation is the ultimate edge – and how cloning helps you turn over more rocks, faster, without sacrificing depth or originality.

• How to clone intelligently – including which legendary investors are actually worth studying (spoiler: few are) and what to watch out for when interpreting their 13F filings.

• The modern twist – how private investors today can go beyond the billion-dollar names to clone smaller, lesser-known managers who operate with fewer constraints and often more transparency.

• Why cloning is not imitation but synthesis – how to build your own process around it, adapt it to your style, and use it as a framework for continuous learning.

The rest of this post is my attempt to capture what I’ve learned – and what I’m still learning – about shameless cloning in a world where every great investor leaves digital breadcrumbs behind.

The Cloner’s Mindset: Why Originality Is Overrated

In investing circles, few ideas have been as simultaneously misunderstood and underappreciated as “shameless cloning.” The phrase was popularized by Mohnish Pabrai, who has never been shy about borrowing ideas – whether from Warren Buffett, Charlie Munger, or other investors he admires. But cloning, as Pabrai defines it, isn’t mindless mimicry. It’s the disciplined practice of recognizing what works, understanding why it works, and then unapologetically applying it.

Pabrai himself copied Buffett’s original 0/6/25 partnership fee structure, built a concentrated, low-turnover portfolio inspired by the Buffett–Munger playbook, and even described his entire investing philosophy as “being a copycat at scale.” He’s quick to point out that the best ideas in life are almost never new; they’re just reinterpreted for a different context. Whether it’s the way you invest, structure your business, or even decide where to live and how to allocate your time, cloning the best versions of existing ideas can save years of unnecessary trial and error.

I’ve always found that liberating. In a world obsessed with originality, it feels almost rebellious to admit that the smartest thing you can often do as an investor might be to copy.

Most people equate imitation with intellectual laziness. But in markets – where the scoreboard never lies – it’s the results that matter, not whether the idea was “yours.”

If you can identify what works for those who have already proven their skill, why reinvent the wheel? As Charlie Munger once put it, “Warren has made a number of people rich who have just copied his investments.” The data backs him up. A study by researchers at the University of Nevada, Las Vegas, found that if you had started cloning Buffett’s trades one month after Berkshire’s public filings in 1976, you would have compounded at roughly 24% annually for the next 30 years. That’s an extraordinary outcome for someone doing little more than following a public trail.

But cloning is more than a shortcut to performance. It’s a mindset. It requires humility – the willingness to accept that others may be better equipped to find and analyze great businesses than you are. It also demands patience and discernment. You can’t copy just anyone. You need to filter for the few who’ve shown skill, integrity, and alignment with your own philosophy.

I often think of investing as a lifelong apprenticeship. The difference is that your mentors don’t even need to know you exist. The world is full of silent teachers: Buffett, Akre, Marks, Vinall, Klarman, and countless others who share their thinking in letters, interviews, and filings. Their decisions are public case studies waiting to be reverse-engineered. Learning from them is one of the greatest free lunches in finance – if you have the discipline to do the work.

The irony is that cloning requires more thinking, not less. It’s easy to copy blindly, but it’s hard to copy intelligently. To do it well, you need to understand the reasoning behind each investment, not just the ticker. As they say, “you can borrow ideas, but you cannot borrow conviction.”

You must ask: what’s the thesis here? What are the key variables? What time horizon or risk tolerance does this investor operate under?

Once you’ve done that work, cloning becomes a framework for learning. It’s how you internalize others’ wisdom without paying the tuition of their mistakes.

That’s what being a modern shameless cloner is about. It’s not about giving up your individuality – it’s about anchoring it in proven foundations. As investors, we’re in the business of making probabilistic bets about the future. Why wouldn’t you start with the people who’ve already shown a consistent ability to be right more often than they’re wrong?

From Rock Turning to Copying Greatness

One of the most underappreciated skills in investing is simply finding ideas. Peter Lynch famously said that investing is about “turning over as many rocks as possible,” because somewhere underneath, one or two might hide a gem.

Most investors never build that muscle. They spend all their time polishing the few rocks they already know, rather than searching for new ones.

Idea generation should sit at the heart of every investor’s process. The more ideas you expose yourself to, the more patterns you begin to recognize – patterns in business quality, capital allocation, and valuation that train your intuition. Over time, that pattern recognition becomes a genuine edge.

But here’s the problem: turning over rocks takes time, and the supply of new rocks is almost infinite.

That’s where cloning comes in.

When I first started investing, I did what everyone does: ran stock screeners, scrolled through lists of 52-week lows, and browsed financial media for inspiration. These tools are useful, but their outputs are often noisy. Great businesses rarely screen well as I discussed in this recent write-up (and present a workaround).

Long-term compounders usually don’t look optically cheap on backward-looking metrics. As a result, many of the market’s long-term compounders never even show up in a traditional screener. You have to know where to look, and more importantly, who to look at.

Over the years, I’ve come to rely on a mix of sources for idea generation. I still use quantitative filters occasionally, but the truth is that the most interesting ideas rarely come from a screener. They come from conversations with other investors, posts I stumble upon on X, annual letters I happen to read, or – maybe most often – from shamelessly cloning people who have already done the hard work.

There’s a simple thought experiment that captures the essence of this. Imagine Warren Buffett calls you every quarter to tell you exactly what he bought and sold. That would be an incredibly valuable phone call, right? How much would that be worth to you? Well, the reality is that you already get it … for free. It’s called a 13F filing. Every quarter, professional money managers disclose their holdings. The information arrives with a short delay, but that doesn’t make it useless. Studies consistently show that simply cloning a handful of top-tier investors with a one-month lag can produce above-market returns over long periods.

Cloning also lets you tap into research networks that you could never afford on your own. A top-tier fund manager has access to management teams, industry experts, and analysts that collectively represent hundreds of thousands of dollars in research expense. By studying what they buy, you effectively hire that network for free. It’s a bit like being handed the homework of the smartest kid in class – you still need to understand it, but it gives you a huge head start.

Of course, there’s a spectrum of cloning. At one end are the “dumb copiers” who blindly buy whatever a famous investor buys. At the other are the “thoughtful cloners” who treat each disclosure as an invitation to investigate.

The latter is where you want to be. Blind imitation will never lead to conviction, and without conviction, you’ll sell at the first sign of trouble. But if you use cloning as a starting point – to narrow your universe to ideas already vetted by people you trust – you dramatically increase your efficiency.

Modern cloning isn’t about copying a stock; it’s about copying a thought process. I’m trying to reverse-engineer the logic: what moat does this business have? What earnings power are they underwriting? What return profile must they be seeing to make this position size rational? That’s how cloning becomes a learning exercise rather than a lazy shortcut.

The key is to use cloning to sharpen your own thinking, not replace it. Once you’ve done that, you realize that the difference between “idea generation” and “idea cloning” is smaller than most think. Both are ways of turning over rocks – it’s just that cloning lets you start with the rocks already picked up by the best treasure hunters in the game.

How to Clone Intelligently

Cloning well is an art form. It’s not about copying someone else’s portfolio – it’s about learning to see the world through their eyes. Most investors misunderstand this. They imagine cloning means mindlessly mirroring another person’s trades. In reality, intelligent cloning is about discernment:

• choosing who to learn from,

• understanding how they invest,

• and knowing why their approach fits (or doesn’t fit) your own philosophy.

When I started building my own investing process, I probably followed far too many fund managers. Every quarter I’d open Dataroma or Whalewisdom, scroll through dozens of 13Fs (Dataroma currently tracks portfolios of 81 “superinvestors”), and convince myself that I needed to track almost all of them.

That approach is useless.

The point isn’t to create a watchlist of every manager with a decent reputation – it’s to narrow your field to those a limited number of “GOATs”, those who are truly worth studying, the ones who think like owners, understand valuation like no other, and play long-term games.

Over time, I’ve refined that list of “superinvestors” to a smaller group of investors whose thought processes I find both rational and instructive. These are the “big boys” I used to – more on this below – keep a close eye on during 13F season (the AUM figures are based on their last 13F filings – which do not include non-US holdings):

• Warren Buffett (Berkshire Hathaway) – Portfolio value: $257,521,771,000

• David Einhorn (Greenlight Capital) – $2,325,916,000

• Guy Spier (Aquamarine Capital) – $317,370,000

• Mohnish Pabrai (Pabrai Investments) – $271,602,000

• Chuck Akre (Akre Capital Management) – $10,210,810,000

• Polen Capital Management – $32,586,433,000

• Seth Klarman (Baupost Group) – $4,114,419,000

• David Tepper (Appaloosa Management) – $6,448,962,000

• Bill Ackman (Pershing Square Capital Management) – $13,729,120,000

• Dev Kantesaria (Valley Forge Capital Management) – $4,527,265,000

• Brad Gerstner (Altimeter Capital Management) – $6.93 billion

• Norbert Lou (Punch Card Management) – $309,882,000

• Alex Roepers (Atlantic Investment Management) – $195,982,000

• Francois Rochon (Giverny Capital) – $2,915,806,000

Each of these managers offers something distinct. Buffett is the timeless blueprint of patience and rationality. Einhorn adds a contrarian streak rooted in deep valuation work. Spier represents the temperament side of the craft – the art of doing nothing & staying calm and humble in a noisy world. Pabrai embodies concentrated opportunism, while Akre perfected the “compounding machine” framework of high-return businesses run by trustworthy managers. Polen Capital is a model of quiet discipline and process-driven growth investing. Klarman represents rigor and margin of safety. Tepper and Ackman are masters of opportunistic boldness; Kantesaria and Rochon embody thoughtful, long-duration quality investing. Together, they form an intellectual composite that captures quite a lot of angles of thoughtful stock picking.

Intelligent cloning also means knowing when not to follow. As I wrote above, I used to track the “big boys” mentioned above. But I’ve updated my view on some of them over recent months and years. For instance, I’ve admired Pabrai for years, but his time horizon tends to be shorter. He looks for opportunities that can double in three years and then recycles capital. My own bias is toward long-term compounders – companies I can own through multiple cycles. His track record more recently has also been rather lackluster. I still find Pabrai’s thought process deeply useful, but I don’t copy his trades anymore. Similarly, Howard Marks focuses on credit and distressed assets, while Seth Klarman hunts for spinoffs and event-driven mispricings. So their portfolio activity isn’t necessarily relevant for my idea generation process anymore.

As private investors, we possess many advantages over professional investors (I’m not going to get into the details of this topic here). But one advantage we possess is certainly that our investable universe is much bigger. We have no limits. No limits in terms of size, industry, or geography.

Yet, the fund managers I cited above often do. Many manage multiple billions in AUM.

Nowadays, of the list above, I really only pay attention to the portfolio activity of Ackman, Polen Capital, Rochon, Dev Kantesaria , Norbert Lou, and the Akre Fund team.

The Modern Twist: Cloning in the Age of Infinite Access

The beauty of being an investor today is that there’s almost no excuse for not knowing what the best in the world are doing. Transparency has gone from privilege to default. Fund letters, interviews, podcasts, regulatory filings, social media – they all converge into one enormous open-source intelligence network. If you’re willing to spend a few hours digging, you can follow the footprints of nearly every great investor alive.

That accessibility has completely changed what it means to be a modern shameless cloner. In Pabrai’s early days, you had to wait for print filings or rely on financial media summaries. Today, you can monitor global fund movements in real time. The barriers between professionals and private investors have largely evaporated.

That’s both a gift and a trap. A gift because the information advantage is gone, which means edge increasingly comes from interpretation. A trap because abundance breeds noise, and noise tempts laziness.

For me, the best way to balance those forces has been to move away from cloning billion-dollar managers altogether. Not because they’re not worth studying – they absolutely are – but because the constraints they face often make their portfolios less relevant for smaller, private investors with a much larger opportunity set.

A concentrated fund managing $10 billion can’t really buy a $400 million company, no matter how promising. Its liquidity needs force it into large caps, where opportunity is thinner and competition is ruthless.

That’s why my focus has gradually shifted toward smaller, lesser-followed investors who operate with the same mindset but far more flexibility. These are people who manage smaller pools of capital – often their own money – and who still think and write like business owners. They don’t have marketing departments or investor relations teams. They’re usually a one-man team. What they have is focus, skin in the game, and an investing style that mirrors what most of us as private investors can actually emulate.

Below are 20 of them that I pay attention to, roughly from larger to smaller in scale.

• I included a link to their websites, where you can (usually) find their investor letters

• If available, I included their track record

• the AUM figures may be off at times, but were my best guess based on their latest letters of public filings

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Josh Brown opened the conversation with the kind of introduction few guests could live up to – but Lynch isn’t just any guest. As Brown put it, during his 13-year run managing Fidelity’s Magellan Fund, Lynch achieved a 29.2% annual return, turning $18 million in assets into $14 billion. That record still stands as one of the greatest compounding streaks in market history.

One in every hundred Americans owned the fund. He doubled the S&P 500. Then, at the height of his success – at just 46 years old – he walked away.

Wow!

That alone would make him a legend, but what cements Lynch’s legacy isn’t only performance; it’s generosity. Long before the era of podcasts and Twitter threads, he spent years teaching ordinary investors how to think – not like traders, but like business owners. His books One Up on Wall Street and Beating the Street turned his philosophy into a blueprint for generations of stock pickers.

Before we dive back in, a quick note…

If you enjoy this piece, consider sharing it with a few investing friends – especially those who are still early in their journey. Peter Lynch’s wisdom is timeless, and the more people learn to think this way, the better off they’ll be as investors. It would mean a lot.

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So when he sat down with Josh Brown after decades mostly out of the spotlight, it wasn’t just another podcast episode – it was a window into one of the sharpest minds the market has ever produced.

What’s striking is how current his ideas still feel. Amid AI mania, macro obsession, and a market increasingly dominated by institutions, Lynch’s principles sound almost rebellious in their simplicity.

In this post, I want to break down the key insights from that conversation – the lessons, anecdotes, and truths that still define his thinking today. Whether you’re new to investing or have spent decades in the market, it’s worth slowing down to absorb what Lynch had to say. The available tools have changed, but his wisdom and what truly matters hasn’t – and listening to him now feels like being reminded of how investing was meant to be done in the first place.

Insight #1: Know What You Own & Why the Individual Investor Still Has an Edge

Few ideas define Peter Lynch’s philosophy as completely as “know what you own.” It sounds deceptively simple, yet Lynch treats it as the bedrock of successful investing – the difference between conviction and panic.

In the interview, he retold a story that captures this better than any theory. When Barbara Streisand once called him in a panic about her portfolio, she confessed she was losing sleep and getting up at 5 a.m. to check her stocks. Lynch asked her to name five companies she owned. When she did, he followed up with a single question: what do they do? “She didn’t have any idea,” he recalled. “Here’s an incredibly talented person. She had no idea what those companies did… so what are you going to do if they go down 50%?”

His conclusion was blunt: “If you don’t understand what you own, you’re toast.”

When investors truly understand a business – its economics, products, and balance sheet – they gain the emotional stability to sit through volatility. “You’ll get shaken out if the stock goes from 10 to 8 and you don’t know what they’re doing,” he said. The point isn’t to be fearless but to be informed enough to act rationally.

That’s also why he urges people to be able to explain each holding “to an 11-year-old in a minute or less – not that this sucker is going up.”

If you can’t do that, Lynch insists, “you should buy a fund.”

The principle extends into his enduring belief that individual investors still have an edge. Lynch dismisses the idea that only professionals can succeed. During the interview, he described meeting an audience member who “shovels chicken poop” in New Jersey and yet became a serious, successful investor simply by working hard and studying companies.

“Average people feel they can’t be good at this, but they can,” he said.

The advantage, in his eyes, isn’t data or technology – it’s time, patience, and proximity. Ordinary investors see the world directly. They shop at stores, notice which products are selling, and hear firsthand what’s changing in their industries. As Lynch put it decades ago and reaffirmed here, the edge is right in front of you if you’re willing to look.

This is the mindset that allowed him to turn observations about pantyhose and tacos into major winners. It’s the same mindset he believes still works today. Professionals might have better tools, but the playing field of insight hasn’t vanished. “We were loaded with skill at Fidelity,” he said, “but average people… if they work hard, they’re careful.” The internet has only made that easier. “Now they have company presentations… it’s a lot easier to understand what you own today.”

The takeaway is timeless: knowledge breeds conviction, and conviction beats volatility. You don’t need a Bloomberg terminal, a sophisticated trading algorithm, or a seat on Wall Street itself. You just need to know what you own – and care enough to keep turning over the stones yourself.

Insight #2: The Real Risk – Getting Scared Out, Not Losing Money

Peter Lynch has always believed that the market’s biggest risk isn’t volatility – it’s emotion. What destroys investor returns isn’t a bear market itself but the inability to endure one. In the interview, he repeated one of his best-known lines: “The real key to making money in stocks is not to get scared out of them.”

It’s the kind of statement that sounds almost too simple to matter – until you realize how few investors actually live by it.

Lynch reminded the audience that during his 13-year tenure managing the Magellan Fund, “the market went down ten times… I went down more every time than the market. Every time.” Yet Magellan still compounded at nearly 30% annually. The point wasn’t that he avoided drawdowns; it’s that he survived them. Most investors, he argued, would have sold in despair. He didn’t, because he knew what he owned and why. Conviction is what separates volatility from permanent loss.

He illustrated the psychology of panic with data few people think about: “The average range for a stock on the New York Stock Exchange, the average high, average low every year, is 100%.”

A typical stock might start the year at 20, trade up to 28, fall to 14, and end at 20.

“There’s a 100% move every year,” Lynch said. “Most stocks you’re going to buy, they’re probably going to go down.” That reality – the inevitability of short-term losses – is something investors must internalize, or they’ll never hold on long enough for the thesis to play out.

His advice to younger audiences has always been to write down the reason for owning a stock – the simple story behind it – so when the price falls, you can remind yourself what’s actually changed in the business, not just in the quote.

That’s also why Lynch is skeptical of constant macro chatter and market timing. He knows that volatility isn’t an error in the system – it is the system. “You get recessions, you have stock market declines. If you don’t understand that’s going to happen, then you’re not ready,” he told Josh Brown.

Investors who can’t accept that truth will forever be whipsawed by every correction. The great ones aren’t immune to fear; they just prepare for it in advance.

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Insight #3: Forget Forecasts – Deal With Facts, Not Predictions

Lynch has never had much patience for economists, strategists, or anyone who claims to know what the market will do next. He’s spent his career ignoring them – and the results speak for themselves. “If you spend more than 13 minutes analyzing economic and market forecasts,” he said, “you’ve wasted 10 minutes.”

The line drew laughter from the audience, but the underlying conviction is serious: forecasts are entertainment, not information.

During the interview, he expanded on why macro commentary is such a trap. “Economists predicted 33 of the last 11 recessions,” he joked.

The problem isn’t just accuracy – it’s distraction.

Investors who obsess over predicting downturns end up preparing for ghosts instead of opportunities. “Far more money has been lost by investors preparing for corrections or trying to anticipate corrections than has been lost in corrections themselves,” he said in the same exchange.

The focus on when the market will turn blinds people to the far more important question of what is actually happening in the businesses they own.

Lynch’s process has always been rooted in observable reality. “I just deal with what’s now,” he explained. “What’s happened to used car prices? What’s happened to the price of oil?” He’s not dismissing data – he’s dismissing speculation.

Investors can and should pay attention to facts like employment trends, credit card debt, or savings rates, but trying to project those into the future is, in his view, a waste of energy. “I’d love to know what’s happened in the future,” he deadpanned. “It’s not available.”

He illustrated his point with an anecdote from the housing crisis: “Hats off to the people who did the big short. I had no idea how bad the housing market was, how bad people had second mortgages.” The lesson isn’t that he missed something – it’s that even the most diligent investors can’t consistently forecast macro outcomes. What they can do is assess reality as it exists today and react accordingly.

What I find striking about this mindset is how it scales. For Lynch, the antidote to macro anxiety isn’t ignorance – it’s focus. There’s a humility embedded in his process: the recognition that the future is unknowable, but today is measurable. The best investors, in his view, aren’t prophets; they’re observers who act on the evidence in front of them.

Insight #4: Turning Over Rocks – Where the Real Edge Comes From

When Peter Lynch talks about “turning over rocks,” he isn’t using a metaphor to sound clever – he’s describing a way of life. Research, to him, has always been an act of curiosity and endurance, not prediction. “The person that turns over the most rocks wins the game,” he said in the interview, quoting the line he once used to describe Fidelity fund manager Joel Tillinghast. “If you look at ten stocks, you’ll probably find one that’s mispriced. Look at twenty, you’ll find two. Look at forty, you’ll find four.”

The edge, in other words, comes from looking longer and harder than everyone else.

That spirit of investigative patience has defined his entire career. It began with his early days as a caddy, where he overheard golfers talking about the stocks they owned. “I’d look it up and a few months later they were higher,” he recalled. “This pretty good deal, you know.” That curiosity – the willingness to connect real-world observations to investment ideas – never left him.

Decades later, the same curiosity led him to Taco Bell and Hanes, two of his most successful early investments. He noticed what people were buying, saw how their behavior was changing, and connected the dots before others did. It wasn’t luck. It was legwork.

But turning over rocks is only half the story. The other half is where you choose to look – and for Lynch, that’s often in places others avoid. “Stocks are mispriced when there’s a lot of knowing in the class,” he explained, referring to how crowded ideas tend to be fairly valued. His solution? Look where no one else is looking. “I’ve had companies that were losing six dollars a share and things started getting better. They went from losing six to losing two – still not good, but not as bad. Then they went from losing two to making two. The stock quadrupled. It’s the same four dollars.”

It’s a beautiful example of how contrarianism and fundamental improvement intersect. Most investors only show up once the turnaround is obvious, but Lynch looks for early evidence of progress – the inflection point before the crowd notices.

He understands that when sentiment is universally negative, expectations are already priced for failure. “What were they doing when they went from losing six to losing two? Things didn’t improve, but they were doing something right.”

His story about Waste Management during the interview captured the same principle. “Nobody wanted anything to do with this stock,” he recalled. “Number one, they’re involved with garbage. Number two, everybody thinks the mafia controls it.”

But he saw a company with improving fundamentals and bought in. The stock became one of his big winners.

There’s a quiet defiance in Lynch’s approach. He doesn’t look for comfort; he looks for dislocation. The best opportunities, in his view, come from industries everyone else has written off, where sentiment is broken but business conditions are starting to heal. That’s the essence of turning over rocks – not finding perfection, but finding progress.

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Insight #5: Cutting Flowers, Watering Weeds – How Investors Sabotage Themselves

Arguably, one of the most striking moments in the interview came when Peter Lynch recounted how Warren Buffett once called him out of the blue. “This is Warren Buffett from Omaha, Nebraska,” Lynch remembered. “My annual report’s due in two weeks. I love a quote. Can I use it?” The quote was simple:

“Selling your winners and holding your losers is like cutting the flowers and watering the weeds.”

Lynch replied, “It’s yours.” Buffett later popularized the phrase, but it originated with Lynch – and the philosophy behind it remains one of the hardest disciplines in investing.

Lynch’s view is that human psychology constantly pushes investors toward the wrong actions. The moment a stock rises, people rush to sell so they can crystallize the feeling of being “right.” The moment it falls, they hold on, hoping it comes back to break even. What gets lost is the logic of compounding. A small winner, left alone, can become a multi-decade compounder. A persistent loser, left unexamined, compounds losses instead.

The irony, as Lynch pointed out, is that this behavior isn’t caused by ignorance but by emotion. “You have to have these winners offset mistakes,” he said. Even if you’re only right five times out of ten, a few big winners like “Costco or Walmart or Nvidia” can drive the bulk of returns. Selling them early just to feel safe destroys the math of portfolio growth.

A portfolio, like a garden, needs pruning – but not in reverse. The point isn’t to harvest the healthy parts early and cling to the diseased ones; it’s to let the strong flourish long enough to reveal their full potential.

He’s also quick to remind investors that even the best will be wrong frequently. “You’re terrific in this business if you’re right six and a half times out of ten,” he said. “Even if you’re right five times out of ten, if you own Costco or Walmart … that offsets your mistakes.”

Insight #6: The Modern Market – “I Have Zero AI Stocks“

What made the interview especially fascinating was how Peter Lynch’s timeless discipline met the reality of 2025’s market – a world dominated by AI headlines, trillion-dollar companies, and historically high valuations.

Despite the changes, Lynch’s tone was the same as ever: skeptical of hype, respectful of facts, and completely uninterested in prediction.

“I have zero AI stocks,” he admitted. “I literally couldn’t pronounce Nvidia until about eight months ago.” The audience laughed, but the point wasn’t technophobia – it was humility.

Lynch doesn’t claim insight into things he doesn’t understand, and he doesn’t feel the need to chase what’s fashionable simply because everyone else is.

Instead, he emphasized that every cycle brings its own mania. He saw one in the dot-com era and now sees echoes of it again. “There were so many companies of no value,” he recalled of 1999. “Fortunately, Fidelity didn’t own those damn things.”

His observation about the present moment is subtler. He doesn’t say AI stocks are worthless – just that he has no idea how to value them. That, for him, is enough reason to stay away. It’s a discipline most investors find hard to follow.

Knowing when not to have an opinion is its own form of edge.

He also spoke about valuations with a mix of admiration and disbelief. “Costco has an AI stock multiple, but they sell paper towels,” he said.

The line drew a laugh, but the deeper message was that even great companies can become risky when their excellence is already priced in. He mentioned Walmart trading at 70-year highs in valuation terms, and the broader market hovering around 22 times earnings. To him, this isn’t a doomsday scenario, but a warning: the price you pay still matters, no matter how dominant the business.

And yet, he’s not cynical about big companies. In fact, he expressed open admiration for them. “Facebook or Meta is an incredible company,” he said. “Microsoft’s a great company. Google’s a great company. Amazon’s a staggering company.” What Lynch rejects is the idea that being a great business automatically makes something a great stock.

It’s a subtle but crucial distinction – one many investors ignore during exuberant periods.

He also pointed to structural changes in public markets as another reason for today’s distortions. “Fifteen years ago there were 8,000 public companies. Now there’s about 3,000.” Fewer listed firms means fewer 10-bagger opportunities for stock pickers and more concentration in mega-caps that already dominate indexes. That, in his eyes, explains part of the valuation inflation: the pool of investable ideas has simply shrunk.

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Insight #7: Faith in Progress – Why the System Works

If the interview had a quiet emotional crescendo, it came toward the end, when Peter Lynch shifted from stocks to society.

After a lifetime of watching markets panic, recover, and then hit new highs, his worldview has only grown more optimistic. He doesn’t see fragility everywhere – he sees resilience.

That conviction wasn’t naïve cheerleading. It was built on historical perspective. Lynch reminded the audience that the Great Depression was an entirely different world: no Social Security, no unemployment insurance, no SEC, and an unregulated banking system. “One percent of Americans owned stocks in 1929,” he said. “We had a Federal Reserve that was asleep.” The result was catastrophic. But since then, he noted, every major stress test – oil shocks, inflation, wars, financial crises – has reinforced the system’s strength. “We’ve had eleven recessions,” he said, “and no one’s ever got worse than a five or six percent decline in GDP. There are a lot of cushions now.”

His optimism also extends to technology, particularly the fear that artificial intelligence and automation will destroy jobs. “Automation has been dramatic the last fifty years,” he said. “And we’ve gone from 100 million jobs to 153 million.” He sees innovation as a creator, not a destroyer, and credits the U.S. system for enabling that evolution. “All the growth in this country is entrepreneurs starting a little shop, starting something else. That makes our country great. And the important thing is banks will lend to them.”

Lynch’s humor remained intact even as he addressed existential concerns. He recounted a recent company call where a CEO kept mispronouncing “AI” as “A1.”

“My eleven-year-old grandchild knows it’s artificial intelligence,” he laughed. “There must be people saying it’s not A1, it’s AI!”

His most striking reflection came when he compared the breakup of AT&T in 1984 to the current fears about job displacement. Back then, a million people worked for the company – “one out of every hundred Americans.” Forty years later, the telecom industry employs fewer people, but total U.S. employment is vastly higher. “This has been one of the greatest growth industries,” he said. “We went from a million to 400,000 employees, and yet we added 53 million jobs overall.”

The pattern, in other words, is clear: innovation shifts labor, but it doesn’t erase it.

What makes Lynch’s closing thoughts so powerful is their consistency. He has lived through every modern panic – from inflation in the 1970s to the internet bubble to the financial crisis – and he still sees reason for confidence. Markets correct, economies wobble, but human ingenuity compounds just like good investments do.

His final summary might be the purest distillation of the Peter Lynch worldview: “America creates, China duplicates, and Europe legislates.”

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Conclusion

Listening to Peter Lynch today feels like watching investing wisdom compressed into its purest form. The world has changed – algorithms, AI, and trillion-dollar companies dominate headlines – but his framework hasn’t aged a day.

Know what you own. Ignore forecasts. Do the work others won’t. Let your winners run. Stay patient when fear spreads. And above all, keep faith in progress.

Lynch’s greatness wasn’t just in his returns; it was in how simply he approached a complex world. The tools evolve, but the temperament that drives long-term success remains the same.

For a long time, Twitch was a bit of a black box to me. It was one of those platforms that my students talked about constantly but that I never personally used. They’d mention streamers or live events I’d never heard of, and I’d nod politely, feeling that quiet generational gap you sometimes notice when talking to teenagers.

YouTube I understood. But Twitch? It seemed like something that belonged to a different media universe.

It reminded me of that realization many people my age have when they catch themselves saying what their parents once said about them. For my parents’ generation, the idea of spending five hours going down a YouTube rabbit hole about a random niche topic is almost unimaginable. For mine, Twitch was starting to feel like that same mystery – something that clearly mattered culturally but was hard to intuitively grasp.

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Then the pandemic hit, and, like many people, I started consuming more online content than I’d like to admit. Somewhere along the way, YouTube’s algorithm decided I should watch Counter-Strike videos. Not tutorials or highlight reels – professional matches. And over time, it worked. I developed an interest in the esport, followed some of the teams more closely (spoiler: the two teams I “picked” have mostly been losing ever since), and began watching full games.

That’s when I inevitably ended up on Twitch, since a good amount of tournaments were streamed exclusively there. It felt like stepping into a parallel internet where chat scrolls like a river, emotes replace sentences, and communities operate at lightning speed.

Fast forward to this year, and I started noticing something strange. Certain tournaments I wanted to watch weren’t on Twitch anymore – they were Kick exclusives. I’d never even heard of Kick until then, and yet here it was, somehow attracting major esports broadcasts within a few short years of launching.

That curiosity, in turn, brought me to this post.

What makes the Twitch story fascinating from an investor’s perspective is that I’d never actively thought about it through that lens before. It isn’t a standalone stock; it’s part of Amazon’s vast conglomerate. Within Amazon’s ecosystem, Twitch is too small to move the needle. But as a case study in competitive dynamics, moat management, and platform fragility, it might be one of the most instructive businesses to analyze.

When you study Twitch, the businesses, what should strike you is the density of its network effects. In most digital networks, the logic is simple: more users attract more creators or suppliers, which in turn attracts more users. But live streaming amplifies this in a very particular way. Twitch didn’t just have users and creators – it had communities. And communities compound much faster than networks.

Every additional streamer didn’t simply add more content; they created another micro-ecosystem. Viewers didn’t just consume – they chatted, clipped, donated, moderated, and built inside jokes that made each channel feel like a living organism. That constant feedback between creator and viewer formed the beating heart of Twitch’s moat. It’s one thing to host a library of videos. It’s another to host a living audience that shows up simultaneously, interacts with each other, and then comes back tomorrow.

For years, that structural feature made Twitch feel unbeatable in the live space. By 2021, it commanded around 90% market share in live game streaming. Competitors like Mixer (Microsoft) or Facebook Gaming tried and failed, even with enormous financial backing.

Each underestimated how much of Twitch’s moat was cultural & network-based, not technological. You can build the same servers; you can’t replicate the same inside jokes, memes, or rituals that animate a chat at 2 a.m. during a streamer’s marathon session.

When network effects are this strong, they can’t be broken. They pull creators and viewers into orbit, and once that orbit stabilizes, it’s almost impossible to escape. That’s what made Twitch look, for years, like the perfect compounding asset inside Amazon’s portfolio (Amazon acquired Twitch for $970 million in an all-cash deal in 2014).

The platform’s economics scaled beautifully: minimal marginal cost per stream, strong data network effects feeding discovery algorithms, and the optionality of ads layered on top of subscriptions and bits. From the outside, it seemed like a textbook example of how to convert social energy into financial leverage.

But if you zoom in, there was a fragility built into that structure. Live networks depend not only on scale but on goodwill. A creator doesn’t have to move to a new platform entirely—just streaming a few hours elsewhere each week is enough to fragment their audience, to thin out the density that makes Twitch special. Viewers aren’t permanently locked in either; they’re loyal to people, not platforms. Twitch’s moat looked like an iron wall, but it was really a dense web of voluntary connections. Pull on a few threads hard enough, and the web starts to loosen.

That’s what makes this case fascinating to me as an investor. On paper, Twitch had every structural advantage you’d want: network effects, scale economies, switching costs, brand recognition, and integration inside a deep-pocketed parent. And yet within just a few years, a challenger born in 2022—Kick—was able to seize a non-trivial slice of market share.

To understand how, we have to examine what happens when a company starts treating its moat not as something to nurture but as something to monetize aggressively. Because when you over-extract from a network built on goodwill, the physics change.

When an Advantage Becomes a Tax – How Over-Flexing Weakens the Moat

So at its peak, Twitch was a dream case study in how network effects create near-unassailable dominance.

But the flip side of that dominance is temptation. Once a company realizes it can raise its “take rate” or tighten policies without immediate defection, it often starts testing how far it can push before the ecosystem revolts. In other words, an advantage gradually turns into a tax.

Twitch’s first warning signs were subtle. For years, top creators had enjoyed a 70/30 subscription revenue split – the same basic deal that defined its early success. Then, in 2023, Twitch announced that the standard would shift to 50/50 for most partners.

The move was framed as a rebalancing: higher infrastructure costs, more moderation expense, and a desire to standardize contracts.

But the message creators heard was different. It said:

“We think you have nowhere else to go.”

Around the same time, Twitch introduced stricter ad-placement rules, new guidelines around sponsorships, and moderation policies that alienated segments of its community.

The logic from the inside probably looked rational: higher monetization per viewer, better brand safety, less regulatory risk. Yet, these changes chipped away at the implicit social contract between the platform and its creators. The network that once felt symbiotic began to feel extractive.

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What’s tricky about live networks is that they rarely show early quantitative warning signs. Engagement might stay high even as dissatisfaction simmers beneath the surface. The data lags the mood. A few high-profile streamers leaving doesn’t change aggregate viewership overnight, but it alters sentiment in ways that cascade later. The moment creators start asking themselves whether they have to stream on Twitch or whether they want to, the flywheel begins to wobble.

In hindsight, Twitch’s mistake was managerial more than strategic. The team correctly recognized its pricing power but underestimated how fragile that power was. Network effects don’t exist in a vacuum; they rely on voluntary participation. You can think of it as a loop made of rubber bands, not steel links. Every time you tighten a policy or take a higher cut, you stretch those bands. At first, the tension adds resilience – the system can handle it. But keep pulling, and eventually one snaps.

The cultural fabric of Twitch was also changing. What began as a quirky gaming community matured into a massive commercial ecosystem. With that scale came corporate caution. Twitch had to please advertisers, regulators, and parent company shareholders. The raw authenticity that once defined it was gradually professionalized out of existence. Streamers who thrived on spontaneity found themselves navigating a growing maze of compliance boxes to tick.

Meanwhile, the platform’s engagement metrics had quietly plateaued. By mid-2023, total hours watched across the industry were still growing, but Twitch’s share was slipping. Competitors like YouTube Gaming were stable, yet a new entrant, Kick, was suddenly accounting for meaningful percentages of viewership.

What’s remarkable is not just that Twitch’s share fell, but how fast it happened. Market share data from StreamCharts suggests that between 2022 and early 2025, Twitch’s global live-streaming share dropped from above 90 % to roughly 70–75 %.

For a category that seemed winner-take-most, that’s a structural shift.

That’s the point where the concept of a moat starts to invert. The very advantages that made Twitch powerful – network effects, creator dependence, brand inertia – became liabilities.

Creators were trapped just enough to resent it but not enough to stay forever. Once Kick offered them a credible exit, the migration began.

It reminds me of similar dynamics in other industries. Think of cable companies raising fees until streaming broke their hold, or payment processors that push take rates higher until fintech challengers arrive with cheaper rails.

Dominance blinds management to fragility. What looks like pricing power on a spreadsheet is often goodwill burning in disguise.

In the context of Twitch, the over-flex wasn’t catastrophic overnight, but it created the conditions for defection. By the time management realized that the moat relied on a delicate equilibrium between creator economics and platform profit, that equilibrium was already shifting. And the irony is that this wasn’t inevitable. Twitch could have continued compounding at lower margins for years, preserving goodwill and reinforcing its network density. Instead, it chose to optimize short-term economics – and in doing so, it weakened the foundation that made those economics possible.

That’s also why scale economies shared models are so powerful. Establishing this corporate DNA – and remaining true to it over time – is so damn hard!

Before we dive back in, a quick note…

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Kick’s Counter-Strategy – Capital, Incentives, and Culture

If Twitch’s story is about managerial overreach, Kick’s is about calculated aggression.

The platform didn’t tiptoe into the market – it bulldozed its way in. Launched quietly in late 2022, Kick’s initial brand positioning seemed almost absurdly audacious: an upstart platform backed by the founders of Stake.com, a crypto-gambling site, offering streamers a 95/5 revenue split and near-total freedom of content. The proposition sounded unsustainable. But that was the point.

Kick’s founders understood two things: first, that Twitch had alienated many of its highest-value creators, and second, that live streaming’s economics can be subsidized by deep pockets for a surprisingly long time. Stake.com had billions in annual handle and a young, male, gaming-oriented audience – a perfect overlap with Twitch’s demographics. Kick didn’t need to be profitable; it just needed to look credible long enough to trigger defection.

And it worked. Within a few months of launch, Kick began poaching some of Twitch’s biggest stars. Contracts for xQc, Amouranth, and Adin Ross reportedly reached into the tens of millions of dollars.

But beyond headline names, the broader story was quantitative. Kick’s total hours watched climbed from near zero in early 2023 to roughly 317 million in March 2025, an all-time record. Over the course of 2024, total watch time reached 2.1 billion hours, with an average concurrent viewership of about 258 thousand. That’s roughly double what the platform managed just a year earlier – a 142 % increase in viewership. And the numbers continued to trend higher.

At its peak in early 2025, Kick’s share of the global live-streaming market had crept past 20 %, while Twitch’s fell toward the 70–75 % range. In relative terms, that’s like a decade-old monopoly ceding a fifth of its domain to a company barely two years old.

Kick’s model worked because it turned Twitch’s biggest weaknesses – creator dissatisfaction and cultural rigidity – into its own strengths:

• Where Twitch constrained monetization and moderation, Kick offered freedom.

• Where Twitch cut creator splits to 50/50, Kick flaunted 95/5.

• Where Twitch tightened sponsorship and gambling rules, Kick welcomed them.

To many streamers, it felt like the early, unfiltered version of Twitch all over again.

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There’s also a psychological layer. Live streaming thrives on perceived authenticity. Twitch had become corporate; Kick felt chaotic but personal. It was imperfect, sometimes messy, but alive. Streamers who switched often spoke less about money and more about autonomy. They could design their own sponsorships, stream what they wanted, and interact with audiences without fear of demonetization. That sense of creative control can be just as powerful as economic incentives in shifting loyalty.

Behind the scenes, capital intensity played an enormous role. Live streaming isn’t cheap – bandwidth, CDN distribution, and moderation scale linearly with user growth. But Kick’s backers could absorb the burn. The gambling ecosystem funding it meant that Kick could treat streaming not as an isolated business but as a brand-building investment. In effect, it was marketing spend disguised as a platform. That’s why, even as analysts questioned its sustainability, Kick doubled down on infrastructure, international expansion, and partnerships with esports tournaments.

By early 2025, Kick’s reach had expanded dramatically across Latin America, the Middle East, and Asia – regions where Twitch’s growth had stalled. Non-English-language categories accounted for a rising share of total hours watched, mirroring the globalization pattern that once propelled YouTube.

Now, skeptics will point out that not all of Kick’s growth is “clean.” Reports from analytics firms like StreamCharts and StreamHatchet have flagged inflated metrics and suspected viewbotting. And multistreaming complicates attribution: many creators broadcast simultaneously on both platforms, splitting rather than transferring their audiences. But even accounting for inflated figures, the directional trend is clear. Kick is not just surviving – it’s reshaping the competitive equilibrium of an industry once thought winner-take-all.

That alone reveals something important about moats. A structural moat should, by definition, resist capital-driven assaults. You can pour billions into search and still fail to dent Google. You can spend endlessly on smartphones and still not dethrone iOS. In those cases, money doesn’t buy relevance because the moat is underpinned by user habit, ecosystem lock-in, and product quality.

From an investing standpoint, Kick’s rise is a fascinating anomaly. It’s rare to see a category leader lose this much share this fast outside of regulatory shocks or technological discontinuities.

There was no paradigm shift here – no AI revolution, no new hardware interface. Just capital and culture exploiting managerial complacency. That, to me, makes Twitch’s decline one of the purest examples of what happens when network effects are mismanaged rather than lost.

Investor Takeaways – Managing Moats vs. Milking Them

As investors, we often look for companies with moats so strong that competition feels futile. We prize pricing power, network effects, and customer lock-in because they give businesses the ability to earn high returns on capital for long periods. But what the Twitch case reminds me of is that how a company uses its moat matters as much as the moat itself.

Twitch’s management team had every reason to believe they could flex their power. They had a near-monopoly, a loyal user base, and creator inertia built into their network effects. But they confused the existence of a moat with the permission to exploit it.

Pricing power doesn’t mean you should raise prices. Switching costs don’t mean your partners won’t explore alternatives. And brand equity doesn’t mean goodwill is infinite.

When I think about the companies that do manage their structural advantages well, they tend to share one common trait: (some degree of ) restraint.

What I take away from this episode is a simple but powerful idea: a moat protects you only as long as you protect it back. The moment a company starts viewing its moat as something to harvest rather than cultivate, the advantage begins to decay. Twitch had gravity on its side for years. Then it turned that gravity into a tax. And gravity, once reversed, is hard to stop.

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The back-and-forth had the feel of a timeless debate – one that’s resurfaced in every cycle, from the Nifty Fifty to the dot-com bust to the financial crisis.

As we talked, I kept thinking that the contrast between our views would make for an engaging story, one that could illustrate not only the arguments but also the psychology behind them.

That’s where today’s post comes in. And I got a little creative here. Instead of presenting our exchange directly, I’ve taken a different route. With the help of AI, I’ve reimagined the discussion as a fictional, narrative-driven scene – almost like a chapter from a novel. The characters are imagined, but the ideas and arguments are real, drawn from the heart of our conversation. Admittedly, the story got a bitte little and would probably need more streamlining, but this is more of an experimental post anyway. I’m curious to hear what you think. Don’t expect this format to become a regular feature of the blog, but I thought it would be fun – and perhaps illuminating – to explore the two investing concepts in a more creative way.

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The Night We Drew Two Maps on a Napkin

I met Victor on a Wednesday that smelled like rain. The café had almost emptied, lights dimmed to that amber tone that makes you think thoughts you don’t say out loud during the day. He arrived wearing the expression of a man who has seen three cycles and buried two of them. I’d invited him because I wanted to test my own convictions. He came because he wanted to test gravity. We took the back corner table by the fogged window. The barista had already stacked chairs on tables, leaving ours alone, as if sensing the conversation would need room to breathe. Victor invested like a chess player who always saw five moves ahead and sometimes walked off the board altogether. I tend to hold my pieces until the endgame, trusting that the pawns grow meaner as the board empties.

Between us sat a napkin, a pen, and a question that keeps coming back every decade: do you sidestep the storm or build a house that can take the wind

He began without a greeting. “At scale,” he said, “drawdowns are not marks on a chart. They are years you don’t get back.” He tapped the napkin like it owed him money. “If the next bear is anything like the last true ones, most things go down together. Correlations climb. Liquidity vanishes. You sell what you can, not what you want. You can hold quality and still get your face ripped off.”

The rain pressed harder against the window. It made the streetlights smear. I told him I’ve learned to care less about whether a quote is green next quarter and more about whether the business will earn more five years from now. Volatility bothers me in the way turbulence bothers a seasoned pilot: irritating, sometimes scary, but irrelevant to the destination if the plane is sound. “I don’t have clients who need the money next quarter,” I said. “I have the luxury of time. The error I fear is selling a fantastic business with great long-term expected returns at 12x earnings – way too cheap! – only to watch it compound for a decade while I sit in cash, realizing I’m an idiot because I never re-entered at 9x earnings.”

Victor smiled like people do when they hear a language they once spoke fluently. “We’re not that different,” he said. “I care about long-term compounding as much as anyone. I just think compounding works best when you allow it to avoid amputations. A -50% is not the inverse of a +50%. You need a +100% just to break even. That arithmetic is cruel and silent. It waits decades to punish you.”

I asked him what he was actually willing to do, in real time, to sidestep. He said he would sell. Not trim. Not rebalance. Sell. “I don’t keep score in multiples,” he said. “I keep score in survival. If I smell forced sellers coming, I move to the other side of the table. If I’m wrong, I buy back. If I’m right, I buy back lower. The cost of being early is smaller than the cost of being stubborn.”

I told him stubbornness isn’t the only alternative. There’s also humility. There’s the acknowledgment that you and I don’t get advance notice on where the bottom is or how long the storm lasts. There’s the quiet recognition that many fortunes were not built by perfectly timed exits but by imperfectly timed persistence in exceptional businesses.

He shrugged. “Fortunes were also protected by sitting out the worst innings.”

We fell into a silence that lasted long enough to become a third character at the table. The café’s espresso machine hissed like a distant locomotive.

It was the right time to talk about ghosts.

I said that I keep a running conversation in my head with three people: one who bought early, one who sold early, and one who never touched the account. The first one tends to be the hero in bull markets. The second writes the letters that survive in textbooks. The third is the dull one who ends up strangely rich at 70. Bull markets make the first two loud. Bear markets reveal exactly how much the third was paying attention.

“What about the era when value funds got dragged down with the rest,” Victor asked. “You remember how the ‘cheap’ names didn’t save anyone when the tide went out. If the indices halve, the portfolio that falls forty percent still feels like losing a year of your life.”

More silence…

I told him I don’t worship low multiples. I worship the engine that creates cash without needing fresh equity or wishful thinking. If the engine is intact and the valuation isn’t cheap in my book, my first instinct is not to sell because other people are frightened or could be frightened. My instinct is to re-underwrite. Confirm the unit economics. Talk to a customer if I can. Look for the crack in the story that isn’t in the chart yet. Invert. Always invert my grandfather taught me.

Victor leaned forward. “And if your re-underwriting is fine, and it still falls 50%…?”

I said then the long-term expected return improved, too. It’s only paper losses then.

He shook his head. “You’re thinking like an owner. Markets are public for a reason. Public markets provide liquidity for you to exploit. And markets change behavior. Quickly. Not yours perhaps, but investors are human. If you steward outside capital, the market’s erratic behavior can affect you. You sleep less. You start rationalizing sales to stop the pain. When we discuss strategy, we forget the biology. Drawdowns hijack the nervous system. They break process and turn patience into a slogan people can’t live by. You can talk about your time horizon all you want. In practice, people capitulate. Everyone’s a long-term investor … until they start seeing red!”

I didn’t argue. I have the scars too. There was a year when I watched a position I knew cold get marked down daily for weeks. I stopped opening my brokerage app in public places because my face betrayed me. I remember how many calls I ignored. I remember the moment I almost became a tourist in my own process. Everyone writes about compounding as a math trick. It is also a psychological contest. The prize is not a rate of return. It is the right to keep your method under duress.

Victor uncapped the pen and drew two lines on the napkin. One was a steady upward slope with jagged, shallow dips. The other was stair-steps: flat, then a drop avoided, then a jump up from buying lower, then flat again. “This is me,” he said, pointing to the stairs. “I give up some of the smoothness and some of the trivial rallies to avoid getting trapped in a basement that takes half a decade to crawl out of.”

I took the pen and drew a third line – volatile, a few scary drops, but the slope steeper by the end because the business it represents kept compounding capacity per share. “This is me,” I said. “I’ll take the turbulence for the slope. I can live with a 3x earnings quote for a while if the earning power is increasing by 25% annually for ten years and the capital allocation is sane. Selling because I’m embarrassed by optics is not a game I want to play.”

He nodded, not as agreement but as recognition that we were finally talking about temperament instead of truth. “You’re arguing for identity,” he said. “I’m arguing for arithmetic. For optimizing rate of return. Squeezing out another 1-2 percentage points.”

We traded stories like swap dealers. He told me about an investor he admired who hadn’t had a down year in a decade. “That’s not magic,” he said. “It’s positioning. It’s not marrying anything the market can take away from you in a week. It’s knowing where the exits are, watching the signs, walking sometimes before the music stops. People love to mock market timing until the building’s on fire.”

I countered with the investors I admire who kept positions through multiple panics because their hurdle was simple: is the expected IRR today better than yesterday? They asked fewer questions about the next quarter’s beta and more about the next decade’s capacity to reinvest at high returns. “Holding isn’t laziness,” I said. “It’s a refusal to swap a high-conviction decision for a low-conviction one simply because screens are red. And if I sell, what exactly replaces it? Which opportunity cost am I choosing on purpose?”

Victor countered that the opportunity cost during a crash is time. The time you forfeit to claw back to where you were. “If you step aside,” he said, “you buy back not only shares at a lower price but also years of extra compounding required if you missed the timming. Your financial ambitions? It will take much longer to reach them.”

The barista turned the music down to a whisper and started mopping. Outside, the rain softened to a mist. Victor asked the question that draws a fault line through every portfolio discussion: “If you knew a serious drawdown was coming, would you act differently?”

I said certainty is the one thing I never get. What I get is a siren of signal mixed with noise. I’ve tried to build a life where my process doesn’t require me to be right on the siren’s timing.

He wasn’t satisfied. “But you must have triggers,” he insisted. “You must have a level where selling is not betrayal but prudence.”

I told him my triggers are not levels, they are conditions: debt covenants that bite, customer behavior that changes in ways management didn’t predict, evidence that the moat was a mirage. If those show up, I don’t need a chart. He said his triggers are different: rising dispersion in the internals, liquidity stress that travels quickly, persistent deterioration in breadth without concurrent improvement in forward returns. His were the kind of signals that don’t tell you what to buy but tell you when to leave the party. We took turns sounding like heretics to each other.

He brought up old history, the way investors do when the present is too loud. Someone we both admired raised cash in the late 1960s and waited while the indices bled for years. Someone else kept buying because the businesses were genuinely cheap and still lost three quarters of their capital before any of it mattered. “Which one do you want to be,” he asked. I told him the version of me who will be able to look back and recognize himself. There is a form of success that requires you to become someone you dislike. It doesn’t look like success to me. If I train myself to bail at the first hint of weather, I will become excellent at bailing and very average at owning. If I train myself to sit still when the facts support it, I get better at the weird art of inaction. “The hard part,” Victor said, “is separating stoicism from stubbornness.”

He was right again. Principles and rationalizations wear the same clothes

We drifted into the arithmetic because arguments yield to math. Victor wrote -30% on the napkin and circled it. “From here,” he said, “you need +43% just to get back. Lose -50%, you need +100%. That’s not opinion. That’s math. Downside avoidance compounds faster than upside capture. That’s why the very best long-term records often have fewer deep holes rather than more big winners.”

I took the pen back and wrote +15% repeated, then drew a second series with several -20% interruptions and subsequent +30% recoveries. “You’re not wrong,” I said. “But the math of a portfolio that keeps increasing in intrinsic value is different. The IRR can outrun the geometry of short-term drawdowns if the business quality and reinvestment runway are real.” He smiled. “If,” he said. “If the runway is real. If the capital allocation stays sane. If the competitive landscape doesn’t change because of the very tech we’re all worshiping right now. If your customers still show up. If rates don’t change the business gravity. You see why I prefer to trade the weather rather than bet my life on the airport staying open.”

We laughed because every investor is a meteorologist in disguise. The only difference is whether you carry an umbrella or build a roof. He asked me what I did during the most recent flavor of mania. I told him my exposure to the theme du jour is now minimal. He raised an eyebrow. “So you do time things” I said I shape exposure to avoid owning things whose outcome space is too wide for my taste. That’s a different thing. Owning less of a lottery is not timing. It’s preference.

He said preference is a form of timing when expressed in the portfolio. I admitted he had a point, and we agreed to call it calibration instead.

In the calm that followed, I asked him what he fears most. He said he fears living through a long, grinding market where prices go nowhere for years and years and cash becomes a more honest friend than equities. He fears giving back too much in an attempt to be brave. He fears being the last buyer out of habit. I told him I fear the opposite: creeping into tactical cleverness and waking up as someone who owns nothing long enough for it to matter. I fear the unforced error of selling a compounding machine to avoid feeling foolish. I fear spending my finite energy chasing a quarterly scoreboard that was never the game I wanted to play

He sat back and regarded the napkin with our lines, circles, and drawings on it as if it were a gallery piece, the kind of modern art that makes the viewer do all the work.

“Maybe we’re arguing about dosage,” he said. “How much of each pill you take. Some people can handle the medicine of volatility. Others need the sedative of cash. The danger is prescribing your own dosage to everyone else” I liked that. Investing is personal in ways people pretend it isn’t. Strategies have temperaments baked in. Systems inherit human flaws. The dataset cannot tell you if you are the kind of person who will sleep at night through a -40% mark on a position you once described as your highest conviction. No backtest can simulate your spouse’s expression at breakfast. That is not an argument for doing whatever you feel. It’s an argument for aligning method with constitution so that you can do what you said you would do when conditions make you want to do the opposite

The barista brought the last two demitasses of the night without asking. She set them down like votive candles and left us to the ritual. Victor said something then that I’ve thought about ever since. “Great track records,” he said, “are usually built by sidestepping ruin. Good track records are built by holding quality. Good can become great if you survive long enough, but the leap is often made in a handful of decisions where you choose not to go over the cliff with the crowd.”

I countered that the leap can also be made by buying into a panic instead of sitting in the lobby with everyone else. The arithmetic of -30% is harsh. The arithmetic of buying a dollar of earning power for fifty cents and letting time do the work is also harsh to ignore.

He agreed. “We keep circling the same tree,” he said. “Avoid ruin. Own compounding. The disagreement is which one you prioritize when they seem to conflict.”

We tested each other’s edge cases. If my best business at 12x FCF fell to 6x with no change in fundamentals, would I sell because the market might take it to 4x FCF? I said I would not. I would buy more. And' I’d hope management would do the same, ultimately increasing the value per share.

If his best business at 30x started to decelerate while the market still adored it, would he sell even if the next quarter would likely look fine? He said he would. He would sell precisely because markets love to extrapolate. Somewhere between those answers lives the hinge of a cycle. The investor who demands rerating for comfort exposes themselves to the whimsical nature of crowds. The investor who refuses to consider the crowd’s ability to overshoot exposes themselves to the equally whimsical nature of credit, liquidity, and forced flows.

We began outlining on the napkin a rough code for both philosophies, not because codes are binding but because they make you honest. His code started with the willingness to be early. Mine started with the willingness to be bored. His included a standing promise to raise cash when the internals screamed more loudly than the headlines. Mine included a standing promise to add when the expected return outpaced my discomfort. His cautioned against confusing cheap with safe. Mine cautioned against confusing volatility with risk. We both wrote the same line at the bottom without discussing it:

Do not build a strategy you cannot live with.

The rain stopped. The street outside looked polished. The barista stacked our cups and turned the sign on the door. We didn’t move. Victor asked me for a final word. “If I have to choose,” I said, “I choose to be the person who owns compounding engines through other people’s fear. I accept that this means I will sometimes look wrong, sometimes for longer than I’d like. I accept that a quote can insult me without hurting me if the earning power holds. I accept that cash is comfort but rarely a strategy for me. I am not indifferent to the cycle, but I will not make the cycle my religion. If my heroes have taught me anything, it’s that the market eventually pays owners who mind their businesses.” He nodded and gave me his final word. “If I have to choose,” he said, “I choose to be the person who leaves the party with money in his pocket. I accept that this means I will sometimes leave too soon. I accept that I will look silly in momentum moments. I accept that I will buy back higher if I’m wrong. What I cannot accept is a hole so deep that the next three years of good decisions only repair what one bad decision destroyed. If my heroes have taught me anything, it’s that survival is the secret compounding”

We stood to leave, and I realized the windows had cleared. The city had drawn its breath back. Victor folded the napkin and slid it across the table. “Keep it,” he said. “It’s two maps. You’ll need both.”

Outside, the pavement shone under the streetlight. We walked to the corner without speaking. At the crosswalk, we turned in opposite directions, not as disagreement but as division of labor. He would watch the weather. I would watch the engines. Both of us, in our own way, would be waiting for the same thing: a moment when the world forgets how money is actually made and we remember. That is the moment to act, whether your method is stepping aside or stepping in

I went home and wrote down the rules. Not hard rules. Pilots’ checklists. A promise to re-underwrite businesses as if no quotes existed. A promise to study internals when the surface looked calm. A promise to remember the geometry of losses without letting it scare me into cash as a default. A promise to remember the distortions of gains without letting them seduce me into thinking I’m smarter than the tide. A promise to diagnose my own nervous system before diagnosing the market. The next morning, the market did what it always does. It opened. Screens glowed. Green and red competed. Somewhere, Victor was trimming. Somewhere, I was adding. Somewhere, another investor was doing neither, and time, indifferent as gravity, kept compounding what he already owned

The napkin sits on my desk now. Three lines. None of them straight. None of them guaranteed. All of them paths that can work, or fail, depending on whose hand holds the pen, and when they lift it. If there’s a lesson worth carrying into the next cycle, it’s that opposing schools of thought are often different solutions to the same equation. Choose the math you can live with. Then, when the rain hits the glass and someone you respect tells you to sell it all or to buy with both hands, remember that they might be right for them and wrong for you. Investing is personal, but it rewards those who make personal decisions with professional discipline. You can sidestep ruin. You can own compounding stocks. You can even design a life that allows you to do both, just not perfectly, and never without doubt. The point is not to be spared from storms. The point is to know why you stepped where you did when the street was wet, and to be able to step again tomorrow.

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Personality plays a huge role too. Some investors feel restless without constant activity, others are content to sit on their hands for years – I’m looking at you Norbert Lou and Guy Pier.

But beneath all the variety, I’ve come to believe there are timeless principles you simply cannot ignore if you want to last in this game and beat the market. Chief among them is the ability to stand apart from the herd. Every great investor has looked like a fool at one point or another – sometimes because the market lagged in catching up to their thesis, other times because the call really was foolish. Either way, success demands the willingness to act alone, absorb doubt, and tolerate discomfort.

One particularly interesting dimension where investors differ – and where I want to focus in this piece – is in how much fundamental work they need before pulling the trigger. Some investors require exhaustive research, others rely on a quick synthesis of a few crucial variables. Where you fall on this spectrum can determine not only your results, but also how much stress you endure along the way.

Here’s what I’ll cover:

• Why Druckenmiller’s Nvidia story is such a revealing case study in fast, conviction-driven investing

• How focusing on just a few critical variables often trumps information overload – and why more data can inflate confidence without improving accuracy

• The virtues of the deep diver approach, and why some investors won’t touch a stock until they’ve built conviction through patient observation

• The risks and trade-offs of both extremes – missing opportunities vs. blowing up on half-baked theses

• How to identify where you fall on the continuum, and how to adapt your process as markets and personal circumstances evolve

Chapter 1: The Druckenmiller End of the Spectrum – Speed, Intuition, and Seizing the Moment

When I first listened to Stan Druckenmiller talk about his Nvidia trade, I was struck not just by the outcome, but by the process. He didn’t arrive at the trade after months of deep research or by building a discounted cash flow model. Instead, the seed was planted in a conversation with his young partner in the fall of 2022. The partner argued that all the blockchain hype was going to be dwarfed by artificial intelligence and suggested Nvidia as the way to play it.

Druckenmiller admitted he “didn’t even know how to spell it” (that line gets me laughing out loud every time!). And yet, he bought it anyway.

A month later, ChatGPT exploded onto the scene, giving him the validation he needed to increase his position substantially. By June 2023, he was openly calling AI a megatrend “potentially bigger than the Internet.” That trade worked out spectacularly.

But the fascinating part isn’t the profit – it’s the process. Druckenmiller acted quickly, without full information, and let the conviction build as the evidence rolled in.

That approach embodies a philosophy you see in many great traders: you don’t need every data point to take action. In fact, waiting for perfect information often means missing the move. What matters more is recognizing when the world has shifted, isolating the handful of variables that really drive the thesis, and then betting with size before the rest of the market catches on.

Druckenmiller has a long history of doing this, from shorting the British pound in 1992 to catching the tech wave of the late ’90s. He’s built a career not by having the most complete dataset, but by having the sharpest intuition about turning points.

Of course, intuition is a slippery word. It makes people think of gut feel, or worse, of recklessness. But intuition, in the hands of someone like Druckenmiller, is really a synthesis of decades of pattern recognition. It’s more about pattern recognition than actual gut feeling.

He has spent his life watching markets, internalizing how they behave, and cataloging the subtle signs that precede a major shift. When you’ve put in that kind of time, what looks like “shooting from the hip” is often a deeply informed judgment call.

The challenge, of course, is separating useful intuition from impulsive decision-making. This is where that chart (see below) that Tiho Brkan often shares becomes so relevant.

It shows that as investors consume more and more information, their accuracy barely improves – but their confidence shoots through the roof.

The implication is dangerous. Too much information doesn’t necessarily make you right, but it makes you feel right. And that can be catastrophic.

Druckenmiller sidesteps this trap by cutting through the noise. Instead of drowning in reports, he zeroes in on the two or three KPIs that really matter. With Nvidia, those drivers were crystal clear: AI demand and Nvidia’s position as the only scalable supplier of the necessary GPUs. Everything else was secondary.

Before we dive back in, a quick note…

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The other lesson here is speed. In markets, timing matters. Paradigm shifts don’t wait for your spreadsheets to balance. You can make a lot of money at turning points. But by the time the consensus has built a complete case, the opportunity is often gone.

Druckenmiller himself compared it to buying the Nasdaq in 1999: even if you were right about the long-term impact of the Internet, the entry point made all the difference. The same is true for AI today. If you waited until every major bank had published a deep dive on Nvidia, the stock had already gone from 150 to 900 (before the 2024 10-for-1 split).

But speed cuts both ways…

For every Druckenmiller who nails a turning point, there are countless investors who chase a narrative too early or too late, without enough discipline to exit when the thesis cracks.

Acting fast magnifies both upside and downside. Druckenmiller admitted himself: “I don’t own things for 10 or 20 years. I wish I was Warren Buffett.” That admission underscores the trade-off. His edge lies in moving quickly and decisively, not in holding indefinitely.

If you’re naturally inclined toward this style, the key is to build systems that protect you from the downside of speed. That might mean position sizing smaller until conviction grows, or being ruthless about cutting losses. It might mean constantly asking yourself whether you’re trading intuition or just getting swept up in the zeitgeist.

The difference is subtle but crucial.

When I think about Druckenmiller’s Nvidia story, I don’t just see a lucky trade or a well-timed bet. I see the distilled essence of a style of investing that prioritizes speed, clarity, and conviction over exhaustive analysis. It’s a style that thrives on big themes, asymmetric setups, and the willingness to act before the crowd has even grasped what’s happening.

(Druckenmiller’s Nvidia discussion at the podcast of the Norges Bank Investment Management starts at 12:38 in the video above)

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Chapter 2: The Deep Divers – Patience, Conviction, and the Weight of Evidence

If Druckenmiller represents one extreme of the spectrum, then at the other end you’ll find the deep divers. These are the investors who don’t act until they’ve built a fortress of conviction, brick by brick. They’re not in a rush to own the hottest stock of the moment. In fact, they often let opportunities pass by because they aren’t yet convinced they understand the business well enough.

For them, patience isn’t a tactical choice – it’s the foundation of their process. It’s their strength.

Think of Warren Buffett quietly circling a company for years before making his move. By the time he finally writes the check, he knows the business inside out. He has watched management execute across multiple cycles. He has studied the competitive dynamics, understood the economics in excruciating detail, and assessed the long-term runway for growth. When he does buy, it’s usually with the intention of holding for decades, not months. In practice, he’s exiting most of his holdings fairly quickly, though. So while I’d put Buffett towards the other end of the spectrum discussed, he’s certainly representing the extreme end point.

This approach creates a very different rhythm. Instead of racing to catch a paradigm shift, deep divers cultivate a slow accumulation of evidence. They read annual reports of the last decade (or two if needed) – and then they move on to the competitors and do the same... They attend industry conferences, not for the hype, but to quietly map out how the ecosystem fits together. They track a handful of KPIs over years, waiting to see how management responds when times get tough. The position often starts small, sometimes just a placeholder, but grows as the mosaic fills in.

The advantage of this style is obvious: conviction. When you’ve done this much work, you’re less likely to be shaken out by volatility or short-term noise. You’ve anchored your thesis in fundamental reality rather than in narrative momentum. That makes it possible to hold through drawdowns that would terrify others. If you’ve already thought through the risks, tested the downside scenarios, and know the cash flows can weather storms, then a 30% sell-off looks less like a disaster and more like a chance to add.

But deep diving isn’t without its flaws. The most common is analysis paralysis. When you demand perfect clarity, opportunities often slip away before you can act. By the time you’ve mapped out every competitor, the stock may already have doubled. Another risk is overconfidence in the depth of your own research. Spending years on a company can blur the line between insight and attachment. Sometimes the deeper you go, the harder it becomes to admit you might be wrong.

And there’s another subtle danger: mistaking information for edge. Remember that chart from above – accuracy flatlining while confidence soars as information piles up? Deep divers are especially vulnerable to this trap. More research feels like progress. More files, more transcripts, more conversations with management and other investors. But if the marginal piece of information doesn’t change your thesis, it may just be adding comfort rather than clarity and deeper insight.

Still, there are periods when this style massively outperforms. Consider long stretches of grinding markets where hype cycles burn out and only fundamentals matter – it’s been a while, I know... But in those environments, deep divers shine. They’re not chasing fads; they’re quietly compounding wealth through businesses they understand deeply. The gains may come slower, but they often prove more durable, and downsides are limited.

What fascinates me about this style is how personality-driven it is. Some investors simply can’t operate without certainty. They need the security of knowing they’ve covered every angle before risking capital. For them, Druckenmiller’s Nvidia trade would feel reckless, even irresponsible. But for the deep diver, that’s the point: their advantage isn’t speed – it’s endurance, and patience!

They win not by getting in early, but by holding on long after others have lost faith.

Chapter 3: Finding Yourself on the Continuum

When you look at the two ends of the spectrum – the quick shooter who thrives on speed and intuition, and the deep diver who builds conviction through relentless study – it’s tempting to ask which one is better?

That’s the wrong question. The real question is: which one are you?

Self-awareness is the underrated edge in investing. If you’re wired like Druckenmiller, forcing yourself into a Buffett-style waiting game will probably leave you frustrated and underutilized. You’ll end up second-guessing yourself, paralyzed by the need for more information. On the other hand, if your natural temperament is to comb through every filing and stress-test every assumption, trying to mimic Druckenmiller’s rapid-fire style will feel like gambling. You won’t be able to hold on when things get rough, because you didn’t build conviction in a way that suits you.

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What makes this interesting is that neither style is static. Investors evolve.

Most of us, over time, will find ourselves shifting along the spectrum depending on the opportunity set, our life stage, and even our psychological bandwidth. Early in your career, when you’re eager to prove yourself, you might be more of a quick shooter. Later on, with more capital to protect, you might gravitate toward the slow build of the deep diver.

That flexibility is key, because markets themselves don’t sit still. In a fast-moving technological revolution, being able to synthesize quickly and act decisively can be a massive advantage. But in a slow, grinding bear market, the investors who have the patience to sit with deeply researched companies often come out ahead. Knowing when to lean into one style or the other is an art that can’t be outsourced.

There’s also the matter of risk management. If you lean toward the Druckenmiller side, you need systems to keep the downside in check – strict position sizing, willingness to cut losses, clarity on what would invalidate your thesis. If you lean toward the Buffett side, you need to watch for the traps of over-research – getting stuck in analysis, mistaking familiarity for safety, or refusing to sell when the facts change.

The continuum isn’t a binary choice. You can, and probably should, borrow from both ends. Think about this for a moment..

Maybe you start small and fast when a big theme emerges, then gradually scale as your conviction builds.

Maybe you keep a core of long-term compounders you’ve researched deeply, while reserving a portion of your portfolio for opportunistic, quicker trades.

The blend depends on who you are and what you’re trying to achieve.

So if you take nothing else from this, take this: don’t try to be Druckenmiller, and don’t try to be Buffett. Figure out where you sit on the continuum, lean into your strengths, and build safeguards around your weaknesses. That self-knowledge will do more for your long-term returns than any hot stock tip ever could.

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One of the most underestimated challenges in investing isn’t finding opportunities – it’s deciding which opportunities to trust. Every day, investors are confronted with conflicting advice, seductive narratives, and data that can be bent to support almost any conclusion. Some of it sounds intuitively right. Some of it comes wrapped in impressive charts and statistics. Some of it is grounded in the theories of Nobel laureates.

The difficulty lies in knowing which signals matter and which ones should be ignored.

That’s why I’ve come to rely on a simple mental model: the best investment decisions tend to live at the intersection of three cornerstones – common sense, empirical evidence, and theory. Each of these pillars, on its own, can feel persuasive. Common sense keeps you grounded, evidence gives you confidence, and theory offers structure.

But when you lean too heavily on just one, you expose yourself to blind spots:

• Common sense can turn into gut-level prejudice.

• Evidence can be misread or cherry-picked.

• Theory can become detached from reality.

Markets have a way of humbling anyone who relies too narrowly on a single source of conviction. But when all three perspectives align – when an investment makes sense intuitively, is supported by data, and is consistent with theory – you dramatically improve your chances of being approximately right, which is about as much as investing ever allows.

Here’s what we will cover in this post:

• Why investing decisions often get lost in a sea of noise, narratives, and conflicting data

• The three cornerstones of rational decision-making

• How each pillar adds value on its own – and the blind spots it creates when relied on in isolation

• Why the real insight comes from the intersection, where intuition, data, and reasoning align

• A case study on gold, one of the most polarizing assets in investing

• What common sense says about gold: an unproductive asset with no intrinsic value

• What the evidence shows: decades of both spectacular returns and crushing drawdowns

• What theory explains: diversification benefits, store-of-value logic, and the behavioral pull of gold

• Broader lessons for applying the framework to other investment decisions

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Disclaimer: The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

The Framework: Three Cornerstones of Rational Investing

Good judgment in investing doesn’t come from following a single rulebook. It comes from triangulating between different ways of making sense of the world. That’s where the framework of three cornerstones comes in.

By weighing decisions through the lenses of common sense, empirical evidence, and theory, you create a sturdier foundation than any one of them could provide on its own.

Each of these pillars plays a distinct role. Common sense forces you to ask basic, real-world questions. Empirical evidence grounds your thinking in historical reality rather than imagination. Theory gives structure, helping you see patterns and relationships that aren’t immediately obvious.

The magic doesn’t lie in any single one of them. It lies in their overlap – the intersection where intuition, data, and reasoning all point in the same direction.

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A) Common Sense & Critical Thinking – Asking the Obvious Questions

Maybe the most underrated tool in investing is using common sense.

That might sound almost too simple, but the truth is that many costly mistakes come from ignoring what should have been obvious.

Common sense doesn’t mean relying on hunches or following rules of thumb. It means asking straightforward, almost childlike questions that cut through complexity:

• What does this business actually do?

• How does it make money?

• Why will it make more money in the future? How?

• Who benefits from it?

• What would have to happen for it to become more valuable in the future?

• … etc.

Critical thinking sharpens this further by forcing you to question assumptions. Just because something is widely accepted doesn’t make it true.

Common sense might tell you that a company with explosive revenue growth is promising, but critical thinking asks whether that growth is profitable, sustainable, or built on shaky foundations.

Likewise, it challenges you to distinguish between assets that are productive and those that simply change hands at different prices.

This pillar matters because it’s easy to get lost in abstractions. Investors often fall in love with elegant models, complex valuation frameworks, or persuasive narratives (complexity bias … ding, ding, ding! 🔔).

Without common sense and critical thinking as a grounding mechanism, it’s all too easy to confuse noise with insight. Markets are full of sophisticated explanations for things that, on closer inspection, fail the simplest reality test.

The goal isn’t to dismiss complexity – investing is inherently complex – but to use common sense as a filter. It keeps you honest by asking whether your thesis ACTUALLY makes sense outside of spreadsheets and theories. In that way, it acts as the first and most basic safeguard against self-deception.

B) Empirical Evidence – Letting History Speak

The second cornerstone is empirical evidence – the raw record of what has actually happened in markets over time. Numbers don’t tell you everything, but they do tell you something important: how different assets have behaved, what cycles have repeated, and where popular stories have collided with reality.

Relying on evidence helps protect against the dangers of narrative-driven investing. A great story can make an asset sound irresistible, but the data might reveal long stretches of underperformance or extreme volatility. Conversely, an asset that seems boring or outdated may have quietly compounded wealth for decades. Looking at evidence forces you to reckon with reality rather than wishful thinking.

I want to encourage you to read the base rates guide I published recently – I promise it will make you a better investor:

Of course, evidence is not neutral. It can be sliced and presented in ways that support almost any argument. Cherry-picked timeframes, selective benchmarks, or ignoring inflation can make the same asset look like either a star performer or a complete dud. That’s why evidence has to be handled with care – the question is not just what the numbers say, but how and why they say it.

In the end, the role of evidence isn’t to replace judgment but to complement it. It tempers common sense by showing where intuition has been wrong before, and it keeps theory accountable by testing whether elegant models actually line up with reality.

Without evidence, you risk building castles in the air. With it, you give your decisions a grounding in how markets have behaved across time.

C) Theory – The Structure Behind the Chaos

The third cornerstone is theory. While common sense keeps you grounded and evidence shows you what has happened historically, theory gives you a framework for why things work the way they do. It’s the layer that ties together disparate facts and experiences into a system of reasoning. Without theory, you’re left with anecdotes and data points that don’t necessarily connect.

In investing, theory takes many forms:

• Financial theory offers concepts like discounted cash flow, risk-adjusted returns, and portfolio diversification.

• Economic theory explains forces such as supply and demand, monetary policy, and the impact of incentives.

• Behavioral theory adds yet another lens, highlighting the biases and psychological traps that shape markets.

Each of these frameworks gives you tools to interpret evidence and challenge assumptions.

The importance of theory is that it helps you avoid being whipsawed by randomness. Markets are noisy, and it’s easy to mistake short-term patterns for durable truths. A theory gives you a way to test whether what you’re seeing is consistent with a broader principle or just an accident of timing. For example, understanding mean reversion can help you recognize when extreme valuations are unlikely to persist. Grasping the idea of opportunity cost clarifies why holding cash (or a specific security) has a hidden price even when it feels safe.

That doesn’t mean theory is flawless. The risk is always that it drifts into abstraction, becoming detached from reality. Beautiful equations can blind investors to messy details that don’t fit neatly into the model. Theories are tools, not truths. They are useful when they help you interpret the world, but dangerous when you let them dictate reality.

When used alongside common sense and evidence, however, theory adds depth. It transforms raw data into insight and anchors intuition in a structured framework. Together, the three pillars create a balanced lens for decision-making that is stronger than any single one on its own.

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Putting the Framework to the Test – Gold as a Controversial Asset Class

A framework is only useful if it helps make sense of messy, real-world debates. And in investing, few debates are as controversial as the one surrounding gold. For decades, value investors in particular have been split on whether gold deserves a place in portfolios. To some, it is a superstition at best and a waste of capital at worst. To others, it is a proven hedge and a vital store of value.

This makes gold a perfect example for applying the three pillars. It is emotionally charged, it inspires strong arguments on all sides, and it resists easy classification.

By running gold through the filters of common sense, empirical evidence, and theory, we can see how the framework works in practice – and, more importantly, what it reveals when those perspectives collide.

A) Gold Through the Lens of Common Sense & Critical Thinking

If the first filter for any investment is common sense, gold doesn’t make a strong case for itself. It is the quintessential non-productive asset. It doesn’t generate earnings. It doesn’t employ people. It doesn’t invent, distribute, or sell anything. It simply sits there, gleaming.

From a critical-thinking standpoint, that alone is almost a red flag. The core of investing is to allocate capital into activities that create value, that produce cash flow, and that thus can be valued. Gold doesn’t create value – it just changes hands at different prices.

That’s why so many legendary investors have dismissed it outright. Howard Marks once summarized the problem in stark terms:

“There is nothing intelligent to be said about gold. Nobody can tell you the right price for an ounce of gold. People will tell you it should go up or go down. To make any intelligent statements about investments you have to know what the right price is. You can’t do that with an asset like gold, which doesn’t produce any cash flow. So you can buy it out of superstition or ignore it because you are an atheist but you cannot buy it with an analytical foundation.”

Bill Ackman made the same point more colorfully when he contrasted gold with a business:

“I’ve never believed that gold is an investment asset. Would you rather have faith in that, or McDonald’s, which has 32,000 stores?”

The comparison is almost unfair – on one side you have a metal that produces nothing, on the other you have a global franchise machine generating billions in free cash flow each year.

Warren Buffett, as usual, offered a vivid image:

“If Martians were to observe the activity of human beings for many many years, they would not understand one of their activities: They are digging something yellow out from the ground, process them and put them in the vaults and never touch them again.”

His point was that gold fails the “what does this actually do?” test.

And finally, Charlie Munger – who always cut straight to the core – dismissed the entire idea of owning gold as irrational. François Rochon recalls him saying: “I don’t have the slightest interest in gold. I like to understand what works and what doesn’t in human systems. To me that’s not optional; that’s a moral obligation. If you’re capable of understanding the world, you have a moral obligation to become rational. And I don’t see how you become rational hoarding gold.”

Taken together, these views capture why, from the standpoint of common sense and critical thinking, gold looks like a poor investment. It produces nothing, has no intrinsic anchor, and can’t be valued in the same way productive assets can. On this first pillar alone, the verdict seems straightforward: gold is speculation, not investing.

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B) Gold Through the Lens of Empirical Evidence

If common sense pushes us to dismiss gold, history and empirical evidence complicate the picture.

The raw numbers don’t align neatly with the “barbarous relic” narrative. In fact, depending on the timeframe you look at, gold has sometimes been an exceptional performer and other times a devastating disappointment.

The key starting point is 1971. Before that, gold’s price was pegged to the U.S. dollar, which makes any long-term charts that start earlier misleading, or at best difficult to assess. Judging gold’s performance under a fixed price regime is like judging a sprinter’s speed by the years they spent chained to a wall. Once Nixon ended the Bretton Woods system and allowed gold to float freely, it became a true market-traded asset. And from that point forward, the story gets interesting.

Between 1970 and 1979, gold soared roughly 14-fold. Yes, 14-fold! Inflation was raging, the dollar was in turmoil, and investors flocked to the metal as a store of value.

In that decade, stocks barely moved. The S&P 500's performance in the 1970s was characterized by a long bear market and the challenges of stagflation. The result? Negative real returns during a good part of the decade.

But then came the long reckoning.

From its 1980 peak to its trough two decades later, gold lost more than 80% of its value in real terms. An entire generation of investors who bought into the 1970s mania spent decades underwater. In the same period, the S&P 500 – dividends reinvested – went up a lot.

That contrast highlights something essential about gold: timing has mattered enormously. Catch the right decade, and gold looks like the ultimate hedge. Miss it, and you could waste decades of opportunity.

And yet, dismissing gold entirely on that basis misses another chapter. From the early 2000s through the early 2020s, gold staged a remarkable comeback. Across those decades, it outperformed global stock indices and, over certain stretches, even beat the S&P 500. For many investors active in recent memory – including those of my own generation – gold hasn’t been a failure at all. It has quietly served as one of the better-performing asset classes.

The volatility – another empirical data point –, however, is undeniable. Many think of gold as a “safe haven,” but the numbers tell a different story. Gold has often been more volatile than equities, and its drawdowns can be brutal. The maximum peak-to-trough decline since 1980 was around 70%.

So the evidence forces us to rethink. Gold isn’t the useless lump its critics make it out to be, nor is it the ultimate haven its fans claim. Empirically, it’s been both brilliant and disastrous, depending on when you look.

The lesson isn’t that the data challenges our common sense take the reality is far messier than intuition suggests.

C) Gold Through the Lens of Theory

If common sense dismisses gold and empirical evidence complicates the verdict, theory may provide the missing perspective. Financial and economic theory doesn’t turn gold into a productive asset, but it does explain why it persists as a meaningful part of many investors’ portfolios across centuries.

Start with modern portfolio theory. One of the core insights is that diversification improves risk-adjusted returns by combining assets that don’t move in perfect sync. Gold’s lack of correlation with equities and bonds has made it a valuable diversifier in certain market regimes. Even if its long-term return is not that much better than stocks, the smoothing effect on portfolio volatility can, in theory, improve overall outcomes.

That’s why central banks continue to hold gold and why many institutional investors allocate a small-ish portion to it. It isn’t because they expect it to compound like equities, but because it plays a unique stabilizing role, which in turn reduces the risk of making emotion-driven mistakes, improving many investors’ long-term outcomes.

Economic theory offers another angle. Gold has historically functioned as a store of value, particularly in periods of monetary instability. Fiat currencies are backed by governments, which can inflate or debase them. Gold, by contrast, is finite and difficult to produce. From a theoretical standpoint, that scarcity gives it a form of intrinsic monetary value, even if it doesn’t generate cash flows. This perspective is especially prominent in Austrian economics, which views gold as the ultimate hedge against the erosion of purchasing power under fiat money.

Then there’s behavioral finance. Humans have a deep, almost primal attachment to gold. Across cultures and centuries, it has symbolized wealth, security, and permanence. It seems to possess some “intrinsic” value, even though it’s hard to measure.

That psychological pull shouldn’t be underestimated. Investors are not purely rational calculators, and theory helps us understand why assets with symbolic or emotional significance can sustain demand regardless of their productivity. In this sense, gold functions not only as a financial asset but as a social construct that channels human fears and desires.

The theories don’t make gold a perfect investment. They don’t erase the fact that it produces no cash flow. But they explain why it endures and why, at times, it can play a rational role in a portfolio. Viewed through theory, gold is less of an anomaly and more of a tool – one with unique properties that, when understood, can serve specific purposes for investors.

The Intersection – A Nuanced Takeaway on Gold

Looking at gold through each pillar separately leaves you with conflicting conclusions.

• Common sense tells you it’s irrational – a lump of metal with no productive capacity, impossible to value, and therefore speculative.

• Empirical evidence muddies the water – gold has at times been one of the best-performing assets in the world and at other times a wealth destroyer.

• Theory explains why it survives, showing its usefulness as a diversifier, a store of value, and even a psychological anchor for human behavior.

The point of the framework isn’t to let one pillar dominate but to force them into conversation. When you stand at the intersection of all three, a more balanced picture emerges.

Gold isn’t the “perfect hedge” its advocates claim, nor is it the “barbarous relic” its detractors dismiss. It is a tool – one with clear limitations but also undeniable utility under certain conditions.

Through common sense, you recognize it might never compound over the very long run like a business or a bond. Through empirical evidence, you see that its performance can shine in particular decades, crush the performance of equities as it isn’t anchored to cash flow, but disappoint over others. Through theory, you understand why it can still make sense as a small allocation, especially in periods of monetary instability or as a non-correlated asset in a broader portfolio.

This is the strength of decision-making at the intersection. Any one perspective on its own produces a distorted verdict: gold is useless, gold is brilliant, gold is timeless. Only by triangulating between all three do you arrive at a nuanced, rational conclusion: gold can be useful, but only if you understand what it is and, more importantly, what it is not.

And that lesson extends beyond gold. Every investment carries its own mix of intuitive logic, historical evidence, and theoretical justification. When those align, conviction is well-founded. When they don’t, skepticism is warranted.

Gold simply shows in stark relief what is true of all investing decisions – reality rarely fits neatly into one box, and only by integrating multiple lenses can you see it clearly.

What Gold Teaches Us About Decision-Making

The real value of looking at gold through this framework isn’t just about reaching a verdict on gold itself. It’s about seeing how the process exposes blind spots and forces balance. If you relied only on common sense, you’d dismiss gold entirely as a useless relic. If you relied only on the data, you’d conclude that gold is an underrated star performer with decades of strong returns. If you relied only on theory, you might decide it belongs in every portfolio as a diversification hedge. None of those positions, on its own, tells the whole story.

Gold highlights the danger of leaning too heavily on one pillar of reasoning. Common sense without evidence can lead to arrogance. Evidence without theory can lead to misinterpretation. Theory without grounding in reality can drift into abstraction. The strength of the framework lies in forcing those perspectives into tension until they produce a more measured conclusion.

This lesson applies far beyond gold. Every major investment decision involves a mix of intuition, historical data, and theoretical models. When all three line up, conviction can be strong.

When they diverge, caution is warranted!

In fact, the most useful moments often come when one pillar challenges the others. That friction doesn’t create confusion – it creates clarity. It highlights where assumptions need to be questioned, where evidence has been cherry-picked, or where a theory may not apply.

Gold’s story reminds us that investing is never about certainties, only probabilities. I discussed this concept in the piece below:

The goal isn’t to eliminate uncertainty but to approach it with a framework that keeps you rational. Whether you’re evaluating a commodity, a stock, or an entire asset class, the three pillars provide a way to avoid dogma and build decisions on a foundation that is more resilient than any single line of reasoning.

So the next time you face an investment decision – whether it’s a hot growth stock, a defensive utility, or yes, even a bar of gold – try running it through the three pillars. Ask yourself if it makes sense, check what the data says, and see how it fits within the broader theories of finance and economics. The conclusion might not be perfect, but it will almost certainly be stronger than if you relied on only one lens. And in investing, stacking the odds of being “roughly right” is about as close as we ever get to certainty.

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When I first got into investing, I did what almost everyone does: I became a vacuum cleaner. I inhaled everything I could find – books, podcasts, YouTube channels, blogs, Twitter threads, even financial news networks I now consider borderline unwatchable. I figured the more I consumed, the faster I’d level up. More input, more insight, more results. That was the logic.

And to be fair, it wasn’t a mistake. In the early days, that kind of obsessive curiosity is more feature than bug. It’s how you get exposed to different schools of thought. It’s how you figure out who’s thoughtful and who’s loud. And it’s how you start developing your own taste.

But here’s the thing: nobody tells you when to stop vacuuming. And if you don’t, you’ll eventually find yourself stuck with a mind cluttered by contradictions, half-baked strategies, shallow heuristics, and all kinds of seductive nonsense that feels actionable but rarely is. That’s when something has to change.

You stop vacuuming, and you start flushing.

Crude as it may sound, becoming a toilet flush is a much more accurate metaphor for long-term investing success. You start actively discarding ideas, content, frameworks, and even mentors that don’t stand up to scrutiny – or that simply don’t fit your personality. You shift from learning by addition to learning by subtraction. From seeking new inputs to filtering aggressively.

It’s not an easy transition. In fact, unlearning can be far harder than learning. But if you want to build a robust, coherent investment process – one that can survive market cycles and psychological stress – you need to stop hoarding ideas and start curating them.

In this post, I’ll walk you through that evolution:

• How the early vacuum phase seduces almost every investor—and why it’s not all bad

• Why investing is filled with “anti-patterns” that sound smart but consistently mislead

• What makes unlearning so hard—and why it’s where real progress begins

• How to identify and flush what doesn’t serve you

• And finally, how to build your own investing operating system by filtering out the noise and focusing on just a few reliable sources

Before we dive back in, a quick note…

Want to compound your knowledge – and your wealth? Compound with René is for investors who think in decades, not headlines. If you’ve found value here, subscribing is the best way to stay in the loop, sharpen your thinking, avoid costly mistakes, and build long-term success – and to show that this kind of long-term, no-hype investing content is valuable.

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The Vacuum Phase – When More Feels Like Progress

There’s something thrilling about discovering investing for the first time. It’s like someone hands you the keys to a secret world. A world where, with enough knowledge and skill, you can compound your money, escape the rat race, and gain autonomy over your life.

It’s a powerful promise. And that’s exactly why the early phase of an investor’s journey often turns into a full-blown obsession.

So you do what any curious, motivated person would do: you start vacuuming. You read the classics and the trendy new releases. You subscribe to half a dozen newsletters. You binge podcast episodes during your commute and go down YouTube rabbit holes late at night. You search for podcasts with “dividend” in their show’s name. You follow big-name investors on Twitter, get into arguments in comment sections, maybe even dabble in Reddit threads.

It feels like progress. And in some ways, it is. You’re building mental scaffolding. You’re getting a feel for terminology, frameworks, and market history. You’re starting to recognize names, strategies, and opposing philosophies. Growth vs. value, Buffett vs. Greenblatt, index vs. active, technicals vs. fundamentals – over time, it all starts clicking.

The problem is, this phase doesn’t come with a built-in filter. You’re exposed to everything, from timeless wisdom to borderline financial snake oil. Some of it contradicts itself. Some of it sounds brilliant but falls apart under stress. And some of it might work well in theory – but not for you!

Because here’s the first major lesson that gets lost in the vacuum phase:

Just because something works for someone else doesn’t mean it will work for you.

It’s easy to underestimate how much personality plays into investing. Some people thrive in uncertainty. Others don’t. Some enjoy high-stakes concentration. Others sleep better with broad diversification. Some are wired to lean into volatility. Others flinch the moment a stock drops 15%.

None of this is wrong – it just means that fit matters. And early on, you’re not optimizing for fit. You’re optimizing for volume.

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The other hidden danger? Conflicting inputs that erode your ability to think independently. If you follow ten different people who each have a slightly different view of what matters most – revenue growth, ROIC, margin expansion, TAM, insider buying, macro tailwinds, price momentum – you’ll struggle to know which lens to prioritize. You start grabbing concepts off the shelf like ingredients for a meal you’ve never cooked, hoping the end result will taste like wisdom.

It doesn’t.

Instead, you’re left with a cluttered mental model – one that’s too messy to act on, but too familiar to throw out. That’s the point where a lot of investors stall. Not because they lack information, but because they’ve accumulated too much of it – and it’s pointing in too many directions.

The vacuum phase gets you moving, but it doesn’t get you far. To evolve, you have to let go of the idea that more is always better. You have to learn what to keep – and what to flush.

Learning by Subtraction – Why Unlearning Is Harder than Learning

There’s a quote from Charlie Munger that has stuck with me for years: “It’s not the learning that’s so hard. It’s the unlearning.” And if you’ve been investing for a while, you know exactly what he means.

By the time you’ve vacuumed your way through dozens of books, hours of content, and countless market opinions, your brain isn’t just full – it’s cluttered. You’re not operating with a clean, elegant investment framework. You’re carrying around a Frankenstein’s monster of half-digested ideas, conflicting signals, and mental shortcuts that may or may not hold up in real-world conditions.

The issue isn’t just that some of these ideas are wrong. It’s that many of them are almost right. They sound smart. They feel intuitive. They worked once, maybe twice. But over time, you realize they don’t stand up to scrutiny – or worse, they lead you into traps. That’s when the real work begins: unlearning what’s not helpful, even if it once felt profound.

This is where a lot of people get stuck. Because unlearning isn’t just a cognitive task. It’s emotional.

You spent time and energy learning that stuff. You may have built an identity around it – “I’m a GARP investor” or “I follow macro” or “I believe in moats above all else.” Walking away from that isn’t easy. It feels like wasted effort (sunk cost fallacy). It forces you to admit, at least partially, that you were wrong.

And yet, if you don’t do it, you stay trapped. You keep reacting to old signals that don’t work anymore. You keep following outdated rules. You keep investing according to someone else’s philosophy – usually a stranger whose time horizon, temperament, and goals are nothing like yours.

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That’s why the toilet flush metaphor matters. Because flushing isn’t passive. It’s decisive. It’s intentional. You’re saying: “This idea doesn’t belong in my process anymore.” Not because it’s inherently bad, but because it doesn’t help you make better decisions with your capital, in your market environment, with your psychological makeup.

I had a conversation with a friend – Tiho Brkan – who put this perfectly. He told me that early on, he was a vacuum learning machine. He inhaled everything. But now? Now he learns by subtraction. He actively flushes anything that doesn’t work in practice. Especially things that sound good on paper but don’t hold predictive power in the real world.

He called many of them anti-patterns – seemingly smart solutions that are actually harmful. And once you start seeing them, you can’t unsee them.

But before we go there, it’s worth pausing on this idea:

Subtraction is a skill.

It’s not just about skepticism. It’s about discernment.

It’s the ability to spot what’s essential, discard what’s not, and keep your mental operating system clean.

In the end, what separates good investors from great ones isn’t just how much they’ve learned. It’s what they’ve chosen to unlearn.

The Problem with Anti-Patterns – And Why Most Content Is Flawed

By the time you’ve been around the investing block a few times, you start to notice a strange phenomenon: a lot of advice that sounds smart, polished, and well-packaged just doesn’t work. Not in the real world. Not in real portfolios. Not over time.

That’s because a huge portion of financial content is built on anti-patterns – ideas that appear useful on the surface but are ultimately misleading, overly simplistic, or outright counterproductive. It’s what the masses are drawn to.

The term comes from architecture and software design. In those fields, a “design pattern” is a proven solution to a common problem. An “anti-pattern,” on the other hand, looks like a solution but fails repeatedly in practice. It might be popular, it might spread easily, but it doesn’t actually work once it’s implemented.

Investing is full of these.

Take the idea that revenue is everything. You’ll hear this often in growth circles, on Twitter, and even in some venture capital blogs. Revenue, revenue, revenue. Sounds good, right? After all, no company survives without top-line growth.

But as a standalone metric? It’s a terrible compass. It ignores profitability, capital intensity, pricing power, competitive dynamics, and a dozen other variables that actually determine long-term shareholder value.

Yet this “growth is everything” narrative continues to spread, especially in frothy markets. It’s an anti-pattern – a seductive shortcut that skips the hard work of real analysis.

Another common one: “Buy what you know.” Popularized by Peter Lynch, and totally misunderstood. What Lynch meant was to use your personal experience to spot potential opportunities – not to blindly invest in your favorite brands without a rigorous process. But the simplified version is easier to repeat, so it spreads. And suddenly, someone’s building a portfolio of Netflix, Nike, and Starbucks, thinking they’re practicing due diligence.

There’s a reason these anti-patterns thrive: they’re easy to remember, emotionally satisfying, and promise clarity in a complex world. But investing isn’t simple. It’s messy. It involves trade-offs, context, nuance, and most of all, time. Any framework that glosses over those elements should be treated with extreme caution.

The other issue is that content quality is inversely correlated with virality. The more nuanced and thoughtful a piece of research is, the harder it is to package it into a TikTok, a thumbnail, or a 30-second soundbite. Meanwhile, the loudest voices often win attention – not because they’re right, but because they’re confident.

That’s why the flush matters.

You need to develop the instinct to question even good-sounding advice. You need to ask: Does this have predictive power? Has this worked across cycles? Is it compatible with how I actually think and invest? And most importantly: Can I see myself relying on this when the stakes are high and the pressure is real?

Because that’s the test. When the market turns, when your portfolio’s in the red, when the temptation to react is strongest – that’s when anti-patterns do the most damage. They give you just enough confidence to act, but not enough substance to act wisely.

So yes, flush aggressively. But flush thoughtfully. Don’t just discard ideas because they’re trendy or contrarian. Discard them because they don’t serve your process. Because they fail the test of real-world utility. Because they distract more than they clarify.

The goal isn’t to be a contrarian for its own sake. It’s to build a mental operating system that can actually make decisions under pressure. And that only comes from clearing out the clutter.

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Coherence Matters!

At some point, if you stick with investing long enough, you stop trying to copy other people’s playbooks. You stop chasing every new strategy, every hot take, every clever thread. You realize you don’t need more inputs – you need a better filter. Having a strong filter is a superpower. Say “no” to almost everything.

And to do that, you need coherence. A personal investing philosophy that fits your temperament, your goals, your time horizon, and your edge – whatever that may be. Not someone else’s edge. Not someone else’s backtested model. Yours.

This is where subtraction becomes a superpower.

Instead of asking, “What else should I be reading?”, you start asking, “What do I already know that actually helps me?” Instead of following ten different content creators, reading books from 20 different authors, listening to 15 different podcasts – all with conflicting styles –, you narrow it down to two or three whose frameworks you deeply respect. And then you go deep.

That’s what I’ve done. My short list includes Warren Buffett, Charlie Munger, and Michael Mauboussin. Occasionally I’ll throw in someone like Rob Vinall, Geoff (from Focused Compounding), Chris Mayer, or Chuck Akre, but that’s about it. I’ve read almost everything they’ve put out. And I re-read it. Not once. Dozens of times. Not because I’m looking for a new insight every time – but because I want to internalize the mindset, the frameworks, the priorities.

It’s not about chasing novelty. It’s about deepening conviction.

When you read Buffett’s old shareholder letters again and again, you start to hear his voice in your head when analyzing a business. When you study Mauboussin’s work on expectations, complexity, and base rates, you start to see patterns others miss – not because you’re smarter, but because your lens is sharper.

You don’t need fifty inputs. You need five that actually matter. Maybe fewer. What matters is that they align with how you think. That they push you toward clarity, not confusion. That they help you focus on the handful of things that actually drive long-term outcomes – things like capital allocation, moat durability, reinvestment runway, pricing power, and incentives.

And that focus? That’s your edge.

The longer I invest, the more convinced I am that most underperformance comes not from missing great ideas, but from chasing too many of them. From switching frameworks every few months. From second-guessing your own instincts because someone online made a louder case.

It’s easy to forget, but investing is not a group project. You don’t get points for reading the most, following the best thinkers, or using the latest tools. You get rewarded for clear thinking, rational behavior, and patience. And all of those require an uncluttered mind.

So flush what’s not serving you. Re-read what is. And build a system that fits your DNA.

You don’t need more information. You need better filters.

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That might sound obvious at first, but in a world where investors are constantly encouraged to copy proven models, emulate well-known fund managers, or fit their strategy into some predefined style box, it’s easy to lose sight of just how personal this game really is.

Note: The voiceover above is a custom-made, slightly adapted version of the blog post, edited for a smoother and more engaging listening experience. It’s one of the perks available to paid subscribers, as I’m always focused on adding as much value to my subscribers as possible. Enjoy!

I recently listened to a podcast interview with Ian Cassel (linked further below) where he made a point that stuck with me. He said, in essence, that one of the most underappreciated advantages an experienced investor has is their ability to build a portfolio that reflects the full arc of their investing journey. You start out as a beginner making mistakes, chasing what looks cheap, gravitating toward simple ratios and rules of thumb. Then, slowly, you start layering in nuance. You realize the difference between cheap and undervalued. You start to see what really matters in management analysis. You learn the limitations of valuation tools. And you accumulate a kind of scar-tissue wisdom that just can’t be downloaded or shortcut.

What Ian was getting at is that, over time, this layered experience becomes a kind of Swiss army knife. It gives you optionality. It gives you perspective.

And crucially, it gives you the ability to build a multi-faceted portfolio that isn’t overly reliant on a single sector, factor, or market regime.

That’s a serious edge – especially in a world where things are constantly shifting and arguably shifting more quickly than in the past.

This really resonated with me. Because when I look at how my own portfolio has evolved over the years, I can see this very process unfolding in real time. Early on, I was drawn to deep value names – the optically cheap stocks that seem too good to be true. Over time, I developed more respect for quality, for sustainable competitive advantages, and for the kind of growth that is more durable can compound for years.

I learned to blend intuition with analysis, and to broaden my circle of competence without stretching it.

And I started thinking about portfolio construction not just as a question of what fits together numerically, but what fits together experientially.

In this post, I want to expand on Ian Cassel’s point and offer my own thoughts on what it means to build a portfolio that’s grounded in your experience. Not just your market experience, but your personal intellectual trajectory – what you’ve learned, unlearned, and internalized. I’ll explore how early mistakes can evolve into an investing edge, how to avoid being trapped by a single style or thesis, and how to construct a portfolio that is not only more resilient, but less emotionally taxing.

Ultimately, this is about leaning into what you actually know – not what others say you should know. Let’s dig in.

The Lure of Cheapness and the Limits of Backward-Looking Analysis

Most investors don’t start out with an admiration for high-quality compounders or nuanced frameworks for assessing capital allocation. They start with something more visceral: the thrill of finding a “bargain.” And the market, of course, is more than happy to serve up plenty of what look like bargains (emphasis on “look like”).

It’s an understandable entry point. You're new, you're eager, and you're looking for some sort of anchor in a sea of information. Price-to-earnings ratios, book value multiples, and screens for low valuation metrics offer a sense of control. They give the illusion of objectivity – a way to navigate the chaos with hard numbers. Many of us get seduced by this. I certainly did.

“Cheap” stocks, especially those trading at single-digit earnings multiples, seem like low-risk, high-reward bets. The math looks compelling, the downside appears limited, and there’s a contrarian badge of honor that comes with buying what others are avoiding – I call this “contrarian bias” (or another form of this may be “complexity bias” – a tendency to look for ideas that are overly complex, making you feel smart).

But over time, most serious investors realize that this approach has serious limitations. Multiples are not predictors. They are snapshots of the past. And more often than not, there’s a reason those numbers are so low.

You learn, often the hard way, that stocks trading at 5x earnings aren’t always misunderstood gems. Sometimes they’re value traps. Sometimes they’re businesses in structural decline, or ones with hidden liabilities that don’t show up on a balance sheet. Sometimes they’re being priced correctly, and your thesis – rooted in a reversion to the mean – is the actual mistake.

Tiho Brkan recently made a powerful observation that stuck with me. He said that quantitative analysis, despite being the easiest form of due diligence to perform, often leads to the most errors – especially confirmation bias, recency bias, and a neglect of base rates.

His critique wasn’t aimed at the numbers themselves, but at how investors use them. Backward-looking data is alluring because it feels concrete. But markets are forward-looking and often smarter than you are willing to admit. In my recent piece on the wisdom (and madness) of the crowd I wrote:

“The average market participant today is sharper, better-informed, and more process-driven. And with that, alpha has become harder to generate.“

Thus, investing, especially in today’s dynamic and often nonlinear environment, demands an ability to reason probabilistically, not just measure what already happened.

Financial statements are easily extrapolated – and that’s exactly what many investors do, often without realizing it. They take a static snapshot and project it forward. But if the company’s competitive landscape is shifting, or if there’s a new entrant, or if the macro environment no longer favors the business model, those numbers are quickly rendered obsolete. And that’s the trap.

Numbers are comfortable. But markets reward insight, not comfort.

This isn’t to say that valuation doesn’t matter. Of course it does. But if you anchor your process entirely on what’s quantifiable, you’re likely to miss what’s meaningful. And early on in your investing life, you don’t always have the experience to know the difference.

What’s interesting is that this stage – the chase for cheapness – often becomes a rite of passage. It’s not a dead end, it’s a stepping stone. You start with multiple-based valuation because it feels like the most accessible lever. But then, if you’re paying attention, the market teaches you that simplicity can be deceptive. That’s when things start to get more interesting…

Beyond Ratios – Developing Real Investing Maturity

There’s a point in every investor’s development where something quietly shifts. You stop asking, “What does my spreadsheet say?” and start asking, “What’s really going on here?”

The transition is subtle at first. You begin to care less about whether a company is trading at 14x earnings versus 18x, and more about whether the business is structurally advantaged, whether it can keep growing without stretching its balance sheet, or whether management is allocating capital like owners or bureaucrats.

That shift marks the beginning of real maturity. You’re no longer impressed by tidy narratives or statistical anomalies. You’re starting to see investing as a probabilistic exercise grounded in real-world complexity. And with that shift comes a much broader view of what it means to be “right.”

You realize that a compelling thesis isn’t built solely on data – it’s built on judgment. You start weighing things that don’t show up neatly in any model: how incentive structures shape behavior, how customer switching costs evolve over time, how capital-light business models outperform through cycles, or how durable competitive advantages look in motion, not in theory.

In short, you learn to think in stories – but not fairy tales. You build narratives that are grounded in reality, tied to observable behaviors, and flexible enough to adapt when conditions change.

And perhaps most importantly, you start to internalize the humility that good investing demands. You’ve seen enough businesses that looked amazing on paper and then blew up. You’ve probably owned a few.

It’s not that valuation stops mattering. It just takes a different role. Instead of being the driver, it becomes a gauge of expectations. You use it to understand what’s priced in.

Maturity in investing doesn’t mean you’ve figured everything out. It means you’ve learned to sit with uncertainty without rushing to simplify it. You develop conviction not from having all the answers, but from knowing which questions actually matter – and which ones don’t.

This evolution is hard to fake. It doesn’t happen because you read the right books or watch the right interviews. It comes from doing – from engaging directly with businesses, from seeing your hypotheses play out in the real world, and from paying close attention to when they don’t.

And this is where things start to compound. Because every mistake, every success, every hard-earned insight becomes a new lens.

You’re not just building knowledge – you’re refining intuition. And over time, that becomes one of the most powerful investing tools you have.

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The Swiss Army Knife Advantage – Why Diverse Experience Matters

If there’s one thing the market does consistently, it’s change. Regimes shift. Sentiment swings. The factors that worked last year suddenly stop working. And when you’ve tethered your entire strategy to a narrow style or thesis, those shifts can be brutal.

That’s where experience becomes more than just a collection of war stories. It becomes an asset – a form of adaptive range.

You start to understand investing less as a game of fixed rules and more like a landscape that keeps shifting under your feet.

And what helps you stay upright isn’t dogma or rigid specialization. It’s the breadth of tools you’ve accumulated along the way.

Possessing a Swiss army knife … that’s exactly how it feels when you’ve spent enough years in the market, getting your hands dirty in different industries, market caps, governance setups, and business models.

You start to build this flexible, multi-use toolkit. Not because you set out to collect it – but because the market forced you to adapt.

Early on, maybe you specialized in commodity plays or banks or net-nets. And for a while, that edge worked beautifully.

Until it didn’t.

You realize that even if you’ve mastered a specific niche, macro regimes can leave you stranded for years.

Just ask any deep value investor how the last decade felt as growth stocks crushed everything else.

Cycles can last longer than your patience. And if you’re running outside capital, longer than your client base will tolerate.

That’s where the value of range kicks in. If you’ve studied high-growth tech, but also picked apart insurance companies, if you’ve understood roll-up strategies and also analyzed family-owned industrials, you’re no longer locked into a single playbook.

You have options.

You can build a portfolio that isn't dependent on one narrow factor regime to thrive.

But it goes deeper than that. This type of experience also helps you make fewer false negatives. When you're only fluent in one dialect of investing – say, low P/E stocks, bank stocks, or recurring revenue SaaS – it's easy to dismiss opportunities that don't speak your language. But when you've built up fluency across multiple business archetypes, you start seeing connections others miss. You can appreciate a founder-led microcap and a capital allocator in a mature industry, not because they're the same, but because you know what excellence looks like in both.

And that’s the real edge: pattern recognition at multiple levels of abstraction. Not just recognizing a great ROIC, but understanding why it exists, how durable it is, and how it fits within a much bigger mosaic of economic forces and business incentives.

Building an Emotionally Less Taxing Portfolio

The other benefit? You’re more emotionally resilient. When one part of your portfolio struggles, the rest might hold up. And more importantly, you’re not thrown into existential doubt because you’ve bet the farm on one style. You’ve seen enough to know that everything has a season.

None of this is an argument against specialization. There are investors who go incredibly deep in one niche and outperform spectacularly. But for many of us – especially those managing our own capital – it can be more sustainable, more flexible, and less psychologically draining to operate with range. Not diluted range. Informed range.

That range isn’t something you can manufacture overnight. It’s a function of showing up year after year, learning from wins and losses, and letting your experience shape how you see the world. Over time, that experience adds up to something far more valuable than a clever thesis: perspective.

Constructing a Portfolio That Reflects Your Journey

Once you’ve spent enough time in the markets, you start to realize something: the portfolio you build isn’t just a function of your ideas – it’s a reflection of who you are, what you’ve experienced, and what you’ve learned to trust. And if you’re intentional about it, that personal history can be turned into a real edge.

A lot of portfolio construction advice out there starts from the outside: optimize for Sharpe ratio, blend uncorrelated factors, keep a core and explore around it, and so on.

Some of that may have its place in portfolio construction theory. But what if you start from the inside? From what you know deeply – not just in terms of sectors or geographies, but in terms of business dynamics, management behavior, capital allocation patterns, or even customer psychology?

That’s where things get interesting.

If you’ve spent years studying platform businesses, or have closely followed capital allocators who grow through M&A, or if you’ve had front-row seats to the evolution of a highly regulated industry – why not lean into that? Not to the exclusion of everything else, but as an anchor. Your knowledge didn’t appear out of nowhere. It was earned, shaped by mistakes, by cycles you lived through, by positions you held too long or cut too early.

The key is not just to avoid over-specialization – it's to diversify intelligently based on your cumulative insight.

That might mean mixing business models: pairing recurring revenue software companies from Japan with capital-intensive but defensible infrastructure plays listed in the US. It might mean owning both early S-curve growers with massive TAM ahead and mature compounders that throw off cash and quietly repurchases shares at a 15% yield.It could mean including companies across the market cap spectrum, from micro-caps you’ve studied closely to global brands with decades of durability.

Experience-based construction doesn’t mean abandoning structure. In fact, the deeper your experience, the more discipline you tend to bring. You might let your winners run longer because you’ve seen how costly it is to clip compounding early. You might trim a position not because it’s expensive, but because the underlying assumptions have quietly shifted. These aren’t just portfolio decisions – they’re judgment calls that only experience can sharpen.

And that’s the beauty of this approach. You’re not just diversifying to lower volatility or check a box. You’re diversifying through the lens of what you’ve lived through. That’s what makes the portfolio durable. Not just in performance, but in emotional terms. When you know why each position is in there – not just as a stock, but as a type of business you understand – you're less likely to panic when prices drop. You don’t outsource conviction to price action. You own it.

This doesn’t mean your portfolio has to be complicated. Some of the best investors run focused portfolios with just a handful of names. My friend Andrew just increased one of his positions to a size of 40% at cost because he thinks the downside is a 5% compounded return over the long run (we discussed it in this new podcast episode).

But those names reflect decades of pattern recognition. Each one stands on a foundation of familiarity. It’s not the number of holdings that makes a portfolio sophisticated. It’s the quality and depth of thought behind each position – and the coherence across the whole.

So instead of asking, “What should I own right now?” maybe the better question is: “What does my experience qualify me to understand deeply – and what kind of portfolio does that naturally lead to?” Because the truth is, the market will always test you. But the tests are a little easier to pass when the answers are written in a language you’ve learned to speak fluently.

Conclusion – Build the Portfolio Only You Could Build

Every investor’s journey is unique. The scars you carry, the detours you took, the periods where you had conviction and still got it wrong – they all shape how you see the world. That’s not a flaw. That’s your advantage.

We’re so often told to find our style and stick with it. To define ourselves as growth investors, or value investors, or quality compounder aficionados, or special situations hunters. But the longer you stay in the game, the more you realize that rigid labels start to feel like constraints. They lock you into a narrative that may have served you at one point, but doesn’t reflect where you are now.

What Ian Cassel was pointing to – and what I’ve come to believe more deeply with each passing year – is that your experience doesn’t just make you a better stock picker. It makes you a better portfolio builder.

And that’s not just semantics. A great stock picker can find ideas. A great portfolio builder knows how to weave those ideas into something coherent, resilient, and durable. Something that can withstand not just market volatility, but also regime changes and the psychological toll that comes with it if you “niche” underperforms for 5 years+.

Because let’s face it – investing is hard. Not just technically, but emotionally. Even with the best research and the sharpest analysis, you’ll still have to deal with uncertainty, volatility, and doubt. The more your portfolio reflects your own lived experience, the better equipped you are to navigate those periods. Not because you’ll always be right – you won’t – but because you’ll understand why you own what you own. That “why” is what keeps you grounded when things get noisy.

This doesn’t mean you should ignore new ideas or avoid stepping outside your comfort zone. But it does mean being honest about what you understand best – and leaning into that. It means using your experience not as a crutch, but as a compass. Over time, that compass gets more precise. And if you let it, it can guide you to places no screen or style-box ever could.

So next time you’re thinking about what to add to your portfolio – or whether to change course entirely – take a step back. Not just to look at your holdings, but to look at your history. What have you learned to see clearly? Where have you developed real judgment? What blind spots have you chipped away at over the years?

Answer those questions, and the portfolio almost builds itself.

Not from a template. From experience.

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“The longer I invest, the more I come to respect the efficiency of markets.”

That line, from a blog post by Matt Newell, hit home for me. It’s such a simple statement, but it captures something profound that many investors only come to understand with time and experience.

Early on in my investing journey, I used to think the market was filled with idiots. Now I think it’s filled with people who are mostly... not. And that shift in perspective changes everything.

Here’s the fuller passage that grabbed me [emphasis added]:

“The longer I invest, the more I come to respect the efficiency of the stock market. I think a good chunk of the value investing community is stuck in the past — a past where all an investor needs is the right mindset (buying stocks as if you were buying the whole company; be greedy when others are fearful) and the right approach (good business, good management, reasonable valuation). The past in which Buffett, Lynch and others delivered 25% annual returns for decades on end. I think that time is over, and I doubt it's coming back. Most market participants were fucking boneheads back then. Market participants today are a hell of a lot smarter, and a lot more of them have more of a long-term, fundamental approach. Perhaps not a majority, but it needn't be a majority — only enough to push up the price of the really good stuff.”

That hits hard because it says what a lot of investors may be quietly thinking…

The game has changed. It’s not 1965 anymore. It’s not even 2012. The average market participant today is sharper, better-informed, and more process-driven. And with that, alpha has become harder to generate.

Newell’s “solution” is to lean into microcaps – to go where the market is less efficient, where there’s less coverage, less liquidity, and a better chance to find real informational edges. He writes about companies with no analyst coverage, no institutional ownership, and sometimes virtually no trading volume. And I’m not disagreeing with him here. It’s a compelling approach in a more efficient market environment IF you’re wired for it.

But I’m not.

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I Choose a Different Edge

Personally, I attempt to lean into something else: the behavioral edge. Not because I dismiss the informational edge – I respect it deeply – but because I find the risk/reward profile in the microcap space a little too binary for my taste. The trajectory of outcomes tends to be (very) wide, and the average business quality tends to be lower compared to larger-cap companies.

Now, I firmly believe even the world’s LARGEST, most closely followed companies get mispriced. And not just occasionally – it happens all the time!

I’ve written about this before, but I keep returning to a passage Rob Vinall shared in one of his investor letters (see below). It’s one of the most articulate rebuttals to the idea that only small caps can be massively mispriced.

He argues that large- and even mega-cap stocks, while surrounded by round-the-clock coverage and swarms of analysts, can become trapped in consensus narratives that harden into assumptions – price drives narrative. In his own experience, investing in Google during its mobile transition or Facebook amid rising regulatory fear wasn’t about discovering unknown facts – it was about seeing the same facts differently.

Meta’s more recent stock performance may be the best case study to prove his point. The stock is up 7x from the 2022 lows, up 7x over just around 2.5 years, compounding at a triple-digit rate over this period… that reflects some major market inefficiencies (at least when it comes to Meta back in 2022).

When enough people agree on a flawed interpretation, mispricings emerge even in the most visible corners of the market.

That insight has stuck with me. It's not a matter of finding obscure facts – it's a matter of identifying when the dominant interpretation has calcified and detached from reality. That’s where the opportunity lies.

And that brings me to one of the most powerful mental models in investing: the wisdom of crowds – which sometimes break ...

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How Smart Markets Work

Michael Mauboussin has long framed market efficiency through the lens of crowd intelligence. Markets, he argues, function efficiently when three key ingredients are present:

1. diverse perspectives,

2. a mechanism for aggregating information (like a stock exchange),

3. and incentives that reward accuracy and penalize error.

When all three conditions hold, even flawed individuals acting independently can collectively produce remarkably accurate pricing.

The jellybean jar example comes to mind – no single guess is reliable, but the average of many guesses often comes very close to the truth.

But here’s the problem: those conditions don’t always hold.

And the one that tends to fail first is diversity. As soon as people start thinking alike – not just agreeing on outcomes, but using the same mental models, the same inputs, the same narratives – the crowd becomes less of an independent ensemble and more of an echo chamber.

Investors begin standing on the same side of the boat. And that’s when things start to break.

The Fragility of Diversity

What makes this even trickier is that the breakdown is rarely obvious in the moment. Diversity can break down all at once or deteriorate slowly, almost imperceptibly.

Everything may appear stable until one final shift – a sentiment change, a catalyst, a moment of fear – causes a cascade. Other times, the change in sentiment occurs more slowly, but eventually, the crowd shares the same belief.

What’s interesting is that this dynamic doesn’t play out the same way in all market regimes. In bull markets, inefficiencies tend to persist far longer, making it hard to call/time market tops. When prices are rising, narratives are self-reinforcing. The crowd isn’t just optimistic – it becomes increasingly synchronized in its optimism. Dissidents often stay silent or exit the game entirely. There’s no obvious trigger to reset expectations, so valuations and assumptions can drift far from reality, unchecked. That’s how bubbles form – not from a lack of information, but from a temporary breakdown in independent thinking.

Crashes, on the other hand, tend to correct inefficiencies far more quickly – I’m of course generalizing here, but pointing out tendencies. When fear takes over, the market becomes hyper-sensitive to new information, even if that information is incomplete or misunderstood. Correlation spikes. People sell first and ask questions later. Prices overshoot to the downside, not because the facts have changed drastically, but because the wisdom of the crowd has morphed into something more primal.

This idea of fragility shows up in systems beyond markets. In ecology, losing species diversity may not show any visible effects for years – until a small change triggers a rapid collapse. Markets can behave the same way. For long stretches, prices appear rational, signals consistent, volatility low. But underneath the surface, everyone may be anchoring to the same assumptions, chasing the same trades, pricing off the same models.

What I Look For

Inefficiencies show up when that crowd wisdom fractures and the madness takes over. And that happens in different ways: company-specific panics following a single earnings miss or a shorter-term challenge that creates uncertainty, industry-wide fear cycles where entire sectors become untouchable, or market-wide selloffs where risk aversion becomes indiscriminate. Those are the moments I try to exploit. Not with bravado, not with the assumption that I’m smarter than everyone else, but with the willingness to remain rational when others are letting go of the wheel.

In those instances, sentiment detaches from business reality. Expectations compress beyond what is likely or reasonable. And that’s where the behavioral edge shines – if you can stay grounded.

But you still need discipline. Being contrarian alone isn’t enough. You need the calculator too – the valuation discipline, the business analysis, the probabilistic thinking.

That’s why Seth Klarman’s formulation continues to resonate: value investing is the marriage of a contrarian streak and a calculator. If you don’t have both, you’re either reckless or irrelevant.

The Job of the Rational Investor

So what’s our job as an investor? I’d argue it’s two-fold. First, learn to read the crowd. That doesn’t mean dismissing the market’s opinion. Most of the time, it’s right. Markets, when functioning properly, are breathtakingly efficient. Respect that efficiency. But sometimes they aren’t. And your job is to understand when the conditions that make them wise have started to falter.

Second, stay patient. Because those moments are rare. But when they come – when the chorus becomes too loud, when uncertainty gets priced like doom, when good businesses are being dumped simply because they’re associated with a narrative people want to flee – that’s when you step in. Not because you know something no one else does. But because you’ve stayed calm, considered the long-term prospects, kept your edge, and waited for the crowd to lose theirs.

That’s where the opportunity lives for me. Not in obscure corners of the market. Not in complexity for complexity’s sake. But in moments of temporary madness – when a smart system briefly forgets how to think.

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The Missing Link in Growth Analysis?

Growth is the oxygen of any business – and of most investor dreams. A company that grows is often seen as a company that's winning. Most of my readers probably understand that not all growth is “good” growth, and actually creating shareholder value. Growth only becomes value accretive if the returns on reinvested capital exceed the cost of capital.

Now, this shouldn’t be the core focus of this post. Recently, after watching a fascinating interview with investor Chris Hohn, I started asking a simple question that turned out to be surprisingly revealing:

It was an offhand remark by Chris Hohn, founder of the Children’s Investment Fund, that I believe is worth exploring further. He said something to the effect that a 1% increase in price could lead to a 5% increase in profit (5x faster!), depending on your margin structure.

That line stuck with me. It sounded almost too elegant, too powerful to be true. But once I ran the math myself, it was obvious: the underlying logic was sound – and the implications are far-reaching, and I believe, you might gain a real edge if you comprehend the concept.

Most discussions about business quality tend to orbit around a familiar set of metrics: revenue growth rates, profit margins, returns on capital, and so on. These are useful, of course. But I think there's something even more fundamental that often gets ignored. Namely, where the growth comes from – and whether it's structurally repeatable, economically efficient, and strategically defensible.

This post is an attempt to unpack that. It’s not just about pricing power in the abstract or textbook definitions of inelastic demand. It’s about how even small shifts in pricing – when executed by the right kind of company – can result in exponential growth in earnings.

And, by extension, in shareholder value.

Here’s what I’ll break down in this post:

• Why some types of growth are far more attractive than others – and how to tell them apart

• How inelastic demand and uncloneable products create the foundation for real pricing power

• A simple rule of thumb (inspired by Chris Hohn) that explains how a 1% price hike can supercharge profits

• The math behind this rule, and how it plays out across different margin profiles

• Real-world investing implications – including for which setups it matters & how to spot these setups before the market does

If you’ve ever wondered why some businesses seem to generate profit out of thin air – while others grind harder just to stay flat – this post is for you.

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Not All Growth Is Created Equal

Growth may be the universal goal, but its underlying mechanics can be wildly different – and that matters more than most investors realize.

A company can grow in several ways. It might increase the number of units sold, launch new products, expand into new markets, acquire other businesses, buy back its own stock (increasing per share profits), or simply raise prices. On the surface, these all add to the top line – but the implications for the bottom line are very different. Some types of growth are capital-intensive, margin-dilutive, or hard to sustain (or the worst kind of growth: value-destructive).

Others are almost magical in how they flow through the income statement.

The kind of growth I’ve come to appreciate most is the kind that requires the least effort. Not because I’m lazy – but because the market often overlooks how elegant and powerful it can be. And that’s price-driven growth.

Let’s break it down:

• Volume-driven growth can be fantastic – especially when there’s latent demand or geographic expansion potential. Especially, if you can sell more units without having to proportionally increase your costs, you tap into what’s known as operating leverage. Your fixed costs stay the same or increase immaterially while your revenue rises, so profits grow faster than sales. This is one of the most powerful effects in business, especially for companies with high fixed-cost infrastructures: software, platforms, etc.

But even volume growth has limits. It often comes with at least some rising variable costs – logistics, support, COGS – or with significant capital investment. You may need to spend on marketing to acquire more customers, or on CapEx to fulfill demand, and the return on these investments may be rather opaque.

• Then there’s pricing growth – when a company simply raises prices. No change in product. No increase in volume. Just higher prices for the same offering – ideally without losing (a material amount of) customers.

And this is where things get interesting.

If costs remain relatively flat – as they often do in the short term – every extra dollar from a price increase drops straight to the bottom line. That’s pure margin expansion.

And depending on the company’s starting profit margin, even a modest price hike can result in profit growth that far exceeds revenue growth (this is what we will explore further below!).

Some companies are masters at this game. FICO, the credit scoring provider, routinely raises prices for its core B2B products, and clients stay.

Adobe has done it with its Creative Cloud subscriptions.

Ferrari doesn't need to increase unit sales to grow – it can simply raise the price of each car, and demand barely flinches.

Hermes can launch a new version of an existing bag, slap a higher price tag on it, and watch it fly off the shelves.

These are all companies that benefit from what economists call inelastic demand – where customer demand doesn’t meaningfully change even if prices go up. Whether it’s because the product is essential, mission-critical, or imbued with brand prestige, the result is the same: price hikes don’t lead to customer churn. They just lead to more profit.

As an investor, this is gold. And yet, it’s rarely modeled or discussed in most equity research. Analysts will spend hours projecting unit growth and margins, but gloss over the quiet power of a 1% price hike.

That’s a mistake.

Before we dig deeper into why this matters – and walk through the math behind how price-driven growth works for companies with different margin structures – we need to step back and talk about what gives a company that kind of pricing power in the first place. It’s not just about brand or distribution. It’s something more fundamental. Something qualitative. Something that’s hard to replicate.

And that brings us to the next idea: inelastic demand and the uncloneable product.

Inelastic Demand and the Uncloneable Product

A lot of investors obsess over quantitative signals. They build elaborate spreadsheets to compare gross margins, ROIC, free cash flow conversion, net working capital dynamics, and a dozen other metrics that are, no doubt, important. But I’ve come to believe that one of the most underappreciated traits of a truly high-quality business isn’t a number. It’s a qualitative dynamic rooted in the product itself – and how the customer perceives it.

At the heart of this dynamic is inelastic demand.

The basic idea, straight out of Economics 101, is simple: when a product’s price goes up, demand doesn’t fall by much – or at all. That’s inelasticity. We usually see it with things that are essential (like life-saving medication) or with products that occupy a unique space in the customer’s mind. And when a company sells something that customers need, love (that’s a big one!), or can’t find anywhere else, it suddenly gains the upper hand. It can raise prices, knowing that buyers won’t walk away.

Let me take it one step further.

The most valuable businesses, in my view, don’t just sell into inelastic demand – they sell something uncloneable. Something that can’t be easily replicated, substituted, or improved upon by a competitor. Sometimes that uncloneability is functional: the product is mission-critical or deeply embedded into a customer’s workflow. Other times it’s emotional: the product is aspirational, symbolic, or culturally iconic. In the best cases, it’s both.

Think about the Birkin bag from Hermès. You can argue all day about whether it’s “worth” the price – but that’s not the point. The point is, customers don’t want a bag like the Birkin. They want the Birkin. Same with Ferrari. People don’t want “a nice sports car” – they want a Ferrari. That’s not brand loyalty. That’s brand obsession. And when a company reaches that level, it has pricing power in the purest form.

On the functional side, you have companies like FICO. If you're a lender, there’s no real substitute (as of today at least). You need FICO scores, and you’ll pay what it takes, and the cost is immaterial anyhow and also often passed on to customers.

Adobe is similar. Sure, there are alternatives to Photoshop or Premiere, but if you're a creative professional, it's the industry standard. You're not going to overhaul your workflow to save $10 a month. That’s inelastic demand + lock-in. A powerful mix.

And this isn’t just about premium brands or legacy software. Even newer companies can earn this kind of grip. I’d argue that Wix, at its best, built a version of this with small businesses. For a certain type of user, Wix became the easiest, fastest, and most flexible way to get a site up and running for indivual users – and they are now building the same for professional website building agencies.

All of this matters because it leads to one simple outcome: pricing power without volume risk. And once you see it, you can’t unsee it.

You start to realize that inelastic demand and product uncloneability are upstream of most quantitative outcomes. High margins? Usually a downstream effect. Excess returns on capital? Often a result of this dynamic. Durable growth? More likely when pricing power is part of the story.

And yes, I know some investors would argue that management quality trumps all. That a strong team can build or repair almost anything. Maybe. Not disagreeing here. But if you have a truly uncloneable product, backed by inelastic demand, the company can survive a few bad decisions. Hell, it can even survive a donkey running it – as long as that donkey doesn’t destroy what makes the product essential or desirable in the first place.

In short, inelastic demand isn’t just an economic concept. It’s a lens. One that lets you see past the numbers and into the core of what makes a business special – and potentially unstoppable.

So now that we’ve explored the why (it matters), let’s get into the how (it matters). Chris Hohn gave us a clue.

Chris Hohn’s Rule of Thumb, Reframed (and Real-World Implications)

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This post builds on that effort, but now I want to take it a step further. Specifically, I want to understand how efficiently Novo has been reinvesting its incremental capital – not just any capital, but capital earmarked for growth.

In other words, I’m trying to calculate what I’ll call the Return on Incremental Growth Investment, or ROIGI.

Now, here’s where it gets interesting: the returns companies earn on reinvested capital are often much more nuanced than headline numbers suggest. It’s not as simple as plugging a few figures into a spreadsheet and declaring the business a 20% return machine. There are layers to this.

Yes, there’s the well-known distinction between ROIC and ROIIC and how returns tend to evolve as a company moves through different stages of its life cycle. But there’s more. Even within a single business, the returns on reinvestment can vary significantly depending on the type of project, the geography, the business line, or even the competitive dynamics in that particular pocket of the market.

A company might earn incredible returns launching a new product in its home market, while simultaneously burning capital trying to expand in a region where it lacks distribution strength or brand equity.

The headline return number rarely captures that internal dispersion – but it matters. A lot.

So What Are We Trying to Understand Here?

The goal is to estimate how much additional free cash flow Novo Nordisk generated between 2017 and 2024 for every dollar it deployed toward growth, not maintenance.

This is more than an academic exercise. Getting a sense for whether a company is deploying its resources effectively tells you something fundamental about its quality as a compounder. Done right, this can help you distinguish between a stock that has momentum and a business that has structural reinvestment advantages.

Let me be clear: this is not about being precise to the decimal point. That’s not possible. There’s (unfortunately) no official line item in financial statements labeled “growth investments only.” Quite a lot of assumptions will be made. Some lines will be blurry. But that’s okay. My goal is not mathematical perfection – it’s directional truth. I want to get a sense of the return Novo has been getting on its growth spend over this period of substantial reinvestment.

Interestingly, a comment on my previous post addressed the point I’m making with this article. The reader wrote:

“As a slight counter: as a company grows wealthy, it has more money to throw at new ideas. Is it more successful at picking the new ideas? Will the research pan out, just because more resources are going towards it? I would point towards Meta and the buckets it has thrown at virtual reality, which still seems to have as much practical application as crypto at this point.”

This is exactly the question ROIGI seeks to address. What are the returns a business earned on reinvestments more recently – that’s as close as we get to anticipting future returns on reinvestments. How successful has management reployed capital over the last 3, 5, 7, or 10 years?

Just because a company spends more doesn’t mean it earns more. Efficiency matters.

And in an environment where capital isn't free anymore, knowing how well growth capital translates into future cash flow is arguably more important than ever.

So that’s the plan. I'm going to walk you through the framework I used, the assumptions I made, the capital I counted, the cash flow I measured, and the math I ran. Then we’ll try to make sense of what it tells us – and what it doesn’t.

Before we dive back in, a quick note…

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Part 1 – Why ROIGI > ROIC: A Better Way to Measure Growth Returns

Let me start with a confession: I used to think ROIC was the holy grail of business quality metrics. Most investors do at some point in time as they progress as business analysts – they overestimate their abilities (that’s something I discussed in my post on typical learnings trajectories in investing; read it here: “Logarithms, S-Curves, and the Truth About Gaining Investing Expertise!“).

And don’t get me wrong – ROIC still holds value (a lot of value!).

“The most important quality factor, and the one that Mr Market seems to reward the most, is return on capital — surprise surprise! ROC = value creation for all stakeholders of the company. The more value that’s created, the more benefits and rewards.” Tiho Brkan

But once you’ve looked at enough companies and tried to reverse-engineer what actually drives performance, you start to see the cracks.

The biggest issue with ROIC is that it tries to distill something complex and forward-looking – capital allocation – into a tidy, backward-looking, accounting-defined formula. In theory, it captures the return a company generates on the capital it employs. In practice, the number is often distorted by accounting artifacts, acquisitions, and all the stuff that gets dumped into the “invested capital” denominator.

This is where ROIGI comes in. Instead of asking “what is the return on ALL the capital the company has ever deployed,” I want to ask something more focused:

“How well has the company done with its NEW capital – specifically the capital it allocated for growth?”

This isn’t just semantics. It’s a shift in perspective. It separates the legacy business from the incremental engine. And in many cases, it’s the incremental engine that matters most.

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Let’s bring in someone who’s done more thinking on this than most: Aswath Damodaran. In a podcast episode with Patrick O’Shaughnessy, he explained why ROIC often misleads more than it illuminates:

“I know it's become this magic bullet that people focus on. Every year, I actually compute the return on invested capital for every company on the face of the earth, 46,000 companies. So, I'm intimately aware of what accountants can do to screw up that number. I mean, I'll give you a classic example. Forget about using the return capital. Computing the return on invested capital for a company that grows through acquisitions is a nightmare. Why? Because acquisitions create debris. Goodwill, what do you do about that? What do you do if you pay with stock instead of cash? I can make a really bad company have a high return on capital if you give me enough accounting discretion to play that game. […]

The problem with return on invested capital is as a metric, it's designed for mature or declining companies. You can be a great growth company. Its return on invested capital can be either meaningless. […] For young companies, the ROIC becomes almost meaningless. So, if you have an investment strategy built around ROIC, remember, this is going to leave you with a portfolio of older and declining firms. ROIC is a backward-looking accounting number […] Keep that in perspective when your investment strategy is driven entirely by ROIC.”

That last part hit me hard: ROIC is a backward-looking accounting number. Exactly. That’s not what I want to base my thinking on, at least not exclusively.

Instead, I want to look at how effectively a company is deploying new money – capital earmarked for expanding the business, developing new products, entering new markets, or increasing capacity. My ROIGI metric attempts to isolate just that.

And while there’s some messiness involved (which I’ll get into in a moment), it has the potential to tell you a lot more about the health and future of a business than ROIC ever could.

It also has the advantage of being more adaptable to real-world business models. Think about R&D-heavy companies like Novo Nordisk. Most of their “capital” investments don’t show up on the balance sheet – they flow through the income statement as expenses. If you only focus on capitalized assets in your ROIC calc, you’ll miss a massive part of the picture.

puts this well:

“Of course, there are different ways to measure returns (you might use operating income, net income, free cash flow, etc...) and there are many ways to measure the capital that is employed. There are also 'investments' that don't always get categorized as capital investments but run through the income statement—things like advertising expenses or research and development (R&D) costs. To be accurate, you'd want to know what portion of advertising is needed to maintain current earning power (akin to ‘maintenance capex’). The portion above that number would be similar to ‘growth capex,’ which could be included when thinking about return on capital. R&D could be thought of the same way.”

This isn’t easy work. It requires judgment, estimates, and even a bit of courage to make assumptions where the data doesn’t give you a clean split. But in exchange, you get a better sense of what actually matters: how well the company is deploying its incremental resources.

So no – ROIGI isn’t perfect. But I’d argue it’s a step closer to reality than many of the more common formulas people cling to.

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