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Warren Buffett compounded capital at roughly 20% a year for more than six decades.

Sit with that number for a moment, because almost nothing in the history of markets rivals it. Buffett is an absolute outlier in the chart below.

Part of the explanation is temperament, part is structure, part is the simple arithmetic of a very long runway.

Buffett’s edge was real!

But a meaningful slice of it, especially in the early partnership years, came from something we’d now call an analytical edge. He read the Moody’s manuals nobody else bothered to open. He found the net-nets, the obscure insurance floats, the mispriced stubs that were sitting in plain sight because the work of finding them was tedious, and the audience for that work was small.

"[Warren] expends a lot of energy checking out details and ferreting out nuggets of information, way beyond the balance sheet. He would go back and look at the company's history in depth for decades. He used to pay people to attend shareholder meetings and ask questions for him. He checked out the personal lives of people who ran companies he invested in. He wanted to know about their financial status, their personal habits, what motivated them. He behaves like an investigative journalist. All this stuff about flipping through Moody's Manual's picking stocks, it was a screen for him, but he didn't stop there." Alice Schroeder

That world is gone.

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You know this already. Investing has been democratized. Every 10-K is a click away. Every transcript is transcribed, indexed, and searchable within minutes of the call ending. Calls are available in podcast-like apps. Screeners that would have been science fiction in 1965 are free inside your brokerage account. The manuals Buffett had to physically hunt down now arrive pre-digested. Investors share their processes freely online.

And so the intuition almost everyone holds, seasoned investors included, is that the analytical edge has been arbitraged down to something close to zero. The skill ceiling rose so high, and access became so universal, that being smart and diligent stopped being enough. Everyone is smart now. Everyone is diligent.

Then AI arrived, and the intuition hardened into something like certainty.

If the internet flattened the information advantage, generative models look poised to flatten the analytical one. A tool that can read a 300-page filing in seconds, build a discounted cash flow model on request, and summarize the bear case before you’ve finished your coffee would seem to be the final erasure of any thinking advantage a human might once have held. The machine does in ten seconds what took a diligent analyst a week.

Where’s your edge now?

Anne Lundgaard Hansen and Seung Jung Leea thus propose that widespread AI adoption may lead to more efficient markets as bubbles driven by human behavior become less likely.

I want to propose the opposite.

Not as a settled conclusion, because I haven’t arrived at one, but as a hypothesis I think is badly underdiscussed.

The widespread, homogenizing adoption of AI in analysis may not bury the analytical edge. It may be digging it back up. It may not lower volatility, but increase it. It may not lead to less herding, but more crowded trades.

“… when teams simultaneously consult homogeneous AI systems, they unknowingly undergo a process we term Invisible Groupthink. Invisible Groupthink is an AI-mediated conformity that operates without the social pressure or directive leadership that classical groupthink requires, and which is therefore structurally invisible to the organizational safeguards designed to prevent it.“ - Invisible Groupthink: AI Homogenization, Epistemic Diversity, and Organizational Decision Resilience

A quick word on what an edge actually is …

I’ve written at length in a recent post about the distinction between skill and edge, so I won’t rebuild that whole argument here. You can read that piece here:

The short version, for anyone who hasn’t read it: you possess skills, but you do not possess an edge. An edge is external. It’s a situational setup, a behavioral inefficiency, or a structural quirk in the market landscape that appears for you to exploit. It’s an event, not a trait. And the uncomfortable corollary is that you cannot out-skill a bad environment. All the analytical horsepower in the world does nothing if there’s no inefficiency for it to act on.

That framing is highly relevant here, because I believe whether AI makes you more skilled isn’t even a question. Obviously, it does. It makes everyone better.

The question is whether AI creates or destroys the external conditions under which skill converts into excess return.

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It helps to be precise about the kinds of edges available. Bill Miller framed it cleanly, and Nick Sleep later paraphrased him well: there are broadly three competitive advantages in investing.

• Informational, where you know a meaningful fact nobody else does.

• Analytical, where you take the same public information everyone has and cut it up to reach a superior conclusion.

• And psychological, or behavioral, where you are able to think and act rationally when others cannot.

Sleep added the observation that sustainable advantages tend to come from the analytical and psychological buckets, because pure informational edges are fragile and, in regulated markets, often illegal. There’s a fourth edge worth naming, the technical or structural one (I’ll again point you to the write-up linked above).

The informational edge, for the ordinary investor, is mostly dead. Regulation and the internet saw to that. Which leaves the analytical and the behavioral as the only edges a fundamental investor can realistically build a career on.

And the analytical one is precisely the edge everyone now assumes AI has finished off.

The edge that eroded before anyone typed a prompt

To understand what AI might do, it’s worth being honest about what already happened without it. The analytical edge didn’t wait for large language models to start decaying. It had been thinning for decades.

Think about the sequence. Decimalization narrowed spreads. Regulation FD, back in 2000, killed the selective-disclosure game where a favored analyst got the nod on the quarter before everyone else. EDGAR put every filing online for free. Then came the screeners, the aggregators, the transcript services, the research terminals, and eventually a generation of investors raised on all of it who think of a fully-built reverse DCF as table stakes.

The barrier to competent analysis fell and fell.

Here’s the thing that gets missed, though. When a skill becomes universal, it stops being an edge, but it doesn’t stop being necessary. You still have to do the work. You just no longer get paid for doing it, because so does everyone else.

“Financial firms are contending with the most significant overhaul of technology capabilities in a generation. AI-driven digital transformation is reshaping job roles and investment processes, prompting professionals to reconsider the boundaries between human and machine cognition, while firms work to upgrade their technology stacks and human capital to remain competitive. Amid this shift, firms and professionals must reevaluate the skills needed for success.“ - CFA Institute in “AI in Finance: Changing Workflows, Growing Demand for Human Judgment“

So by the time AI showed up, the patient was already pale. Most thoughtful investors had made a quiet peace with the idea that their durable edge, if they had one at all, was behavioral. Time arbitrage for instance. The willingness to be patient when the institutional world structurally cannot be, to hold through drawdowns that force a fund with quarterly redemptions to sell. To step in the ring when everyone is panicking.

If universal access to information compressed the informational edge, and universal access to analytical tooling compressed the analytical edge, then generative AI looks like the same movie with the resolution turned up. The tool doesn’t just hand you the filing. It reads the filing, cross-references it against eight years of prior filings, flags the change in revenue-recognition language, drafts the model, writes the bear thesis, and stress-tests your bull one.

What took a skilled analyst a focused week now takes an afternoon, and the analyst on the other side of your trade has the same afternoon.

The empirical trend supports the worry. There’s a growing body of research, and a growing chorus on Wall Street, warning that as more investors lean on similar models they converge on the same conclusions. From hedge funds to wealth managers, the industry has embraced AI in search of an edge, and researchers are now asking what happens when everyone turns to similar models to find one: buying the same stocks, reacting to the same headlines, and making the same mistakes. Several recent studies suggest widespread adoption could shorten the lifespan of profitable trading signals as investors converge on the same opportunities. If a signal that once took years to arbitrage away now gets crowded out in months, the shelf life of any analytical insight collapses. You did brilliant work, the model agreed with your brilliant work, and so did every other model, and the opportunity was gone before you could size into it.

That’s the steelman. Faster synthesis, universal tooling, accelerating alpha decay, everyone converging on the same answer at the same time. If you stop the story there, the analytical edge isn’t just dead. AI held the pillow over its face.

But notice the word doing all the work in that paragraph. Converge.

Convergence is not the problem. Convergence is the opening.

Here’s what we were assuming so far: when everyone’s analysis converges, it converges on the truth. That the machine consensus is correct, and being correct alongside everyone else simply means the reward gets competed away.

If that were true, the analytical edge really would be finished, because there’d be nothing left to be right about that the crowd hadn’t already priced.

But that’s not what the research on these models actually shows. It shows convergence, yes. It does not show convergence on truth. It shows convergence on the mean.

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This distinction is everything, so let me ground it. When researchers measure the creative and analytical output of large language models, they find something consistent and, for our purposes, remarkable. The models don’t just each produce somewhat generic output. They produce similar generic output to one another.

One study by Emily Wenger and Yoed Kenett administered standard divergent-thinking tests to twenty-two different models from distinct model families alongside a group of human participants, and the models produced dramatically less diverse output than the humans, with effect sizes that in social-science terms are enormous.

“We find that LLM responses mirror other LLM responses far more than humans do other humans, even after controlling for key confounding variables.“

The gap didn’t close when researchers switched models. It’s structural.

Different systems, trained on overlapping data with similar objectives, land on overlapping answers. You cannot escape it by using a different provider, because the homogenization isn’t a quirk of one model. It’s a property of the category.

Why does this happen? Two reasons, both baked in. The models are trained to predict the most probable next token, which, by construction, pulls them toward the statistical center of their training data rather than out to the strange, correct, non-obvious tails where real analytical insight tends to be found. The contrarian, non-consensus view. The variant perception.

“Researchers are now asking what happens when more investors turn to similar AI models to find one: buying the same stocks, reacting to the same headlines, and sometimes making the same mistakes. […] AI may make investors faster and more informed while also making their trades more crowded, their systems easier to fool and their risk-taking harder to control.“ - The Straits Times

And the alignment process that makes them pleasant to use, the reinforcement from human feedback, systematically compresses the variance of what they’ll say.

The output gets smoother, more agreeable, more standardized. It regresses toward consensus by design.

There’s even research showing the effect intensifies when a model has access to your prior conversations and profile, tuning its answers toward what it infers you already believe. The tool is engineered to tell you a well-structured version of what everyone already thinks, and to do it more persuasively the longer it works with you.

Now put that back into a market. If a large and growing share of participants outsources the analytical layer to systems that regress toward the same mean and flatter the user’s existing priors, then the market’s collective analysis doesn’t necessarily get sharper.

It just gets narrower.

The dispersion of views that makes markets efficient in the first place starts to collapse into a monoculture. And a monoculture is not an efficient market. A monoculture is a market that has agreed to stop looking in the same places at the same time.

Interactive Brokers visualized the impact of “belief homogenization” beautifully in the illustration below:

Maybe that’s what’s already driving the multi-trillion momentum trades?

So that agreement may create inefficiency? That’s at least a hypothesis to wrestle with I believe. And IBKR does too, and they propose that widespread LLM adoption may also increase volatility, which stock pickers should love:

“A more important risk question is whether they can make a large number of investors interpret the same information in the same way, at the same time. LLMs may become market-relevant not because they create perfect forecasts, but because they compress diverse research processes into similar narratives, rankings, watchlists and execution plans.“ - Can LLM-Based Trading Systems Increase Market Volatility?

That may be the edge, sitting right where the crowd stopped looking.

I’m not alone in reading it this way, which I’ll admit, surprised me when I went looking. The CFA Institute, not exactly a contrarian fringe outlet, published an analysis titled “The Perils of Declining Judgment in the Age of AI“ arguing that as AI-generated research proliferates, consensus forms faster, and that algorithmic systems tend to reinforce dominant paradigms and raise the risk of intellectual stagnation.

“Artificial intelligence (AI) now introduces a paradox. While machines significantly expand our capacity to process information, excessive reliance on automated cognition may erode the epistemic foundations that enabled civilizational progress. […]

Humans are passively consuming AI-generated outputs rather than actively engaging with data. The consequences have become evident in a decline in critical thinking and decision-making skills, or cognitive laziness (Cheng, 2025; Chatterji, 2025; and others). Left unchecked, reliance on automation risks compromising the quality of investment decisions. The question is not whether machines will become more powerful, but whether human decision-making will remain grounded in evidence.“

Their conclusion is almost word for word the hypothesis I’m circling: widespread AI adoption elevates the value of independent judgment and process diversity precisely by making both increasingly scarce.

“Widespread adoption of generative AI in investment management risks diminishing critical thinking, eroding human expertise, reducing innovation, and concentrating decision-making power in AI models, potentially leading to systemic vulnerabilities, poor adaptability, and ethical concerns.“

When the marginal cost of generating consensus analysis falls to nearly zero, the thing that becomes valuable is the analysis that consensus can’t produce. Scarcity moved. It used to be scarce to do the analysis at all. Now it’s scarce to do analysis that departs from the machine.

So the analytical edge doesn’t return in its old form. You don’t win anymore by being the diligent one who read the filing, because the model read the filing too, and so did everyone else’s model.

You win, if you win, by being the one who reads the filing and then thinks something the model structurally cannot think.

The judgment that sits in the tails. The non-conforming question. The read on a management team, a regime shift, or a second-order consequence that doesn’t sit anywhere in the training distribution because it hasn’t happened yet.

So what does a reclaimed edge actually look like?

Suppose I’m right. Suppose the machine consensus is narrow, confident, and increasingly universal, and suppose that narrowness reopens a door that had been closing for thirty years. What do you actually do with that?

The first thing to understand is what the edge is not. It is not out-computing the machine. You will lose that race, badly, and so will everyone else. The model reads faster, models faster, and never gets tired. If your process is to do by hand what the AI does automatically, you don’t have an edge, you have a slower version of the commodity.

The reclaimed analytical edge can be found downstream of the machine’s output. It begins where the consensus ends.

Practically, that means using the tool to find the consensus rather than to form your own view. Ask the model for the bull case, the bear case, the standard framing, the obvious risks. Not because you’ll adopt them. Because now you know, with real precision, what everyone else who queried a similar model is looking at.

That’s the map of crowded ground. Your job is to work out where the map is wrong. Where does the training distribution fail? Usually in the places the data can’t reach: a genuine regime change with no historical analog, a management team whose behavior you can only judge by having sat across from them, a second-order consequence that hasn’t happened yet and so exists in no corpus.

The model regresses to the mean of what has been written. Your edge is the well-founded view about what hasn’t.

This is roughly the “AI plus human” paradigm the CFA Institute has been circling, though I’d put it more sharply than they do.

“As we’ve noted elsewhere, we believe the future will be defined by the complementary cognitive capabilities of humans and machines, characterized by the “AI + HI” paradigm and the continued importance of professional competence.” - Rhodri Preece in AI in Finance: Changing Workflows, Growing Demand for Human Judgment”

The benefits of AI don’t accrue to individuals, they accrue to processes, which is precisely why the individual edge has to come from somewhere the process can’t reach. When earnings-call analysis and filing review get automated, the repeatable, auditable parts of the job become table stakes. What’s left, the scarce thing, is judgment. Human judgment. The willingness to hold a differentiated view and be wrong in public for a while before you’re right for instance.

Which brings me to the part I find least comfortable, and the reason I’m holding this as a hypothesis rather than a thesis. A reclaimed analytical edge is brutally hard to hold. It was hard when it belonged to Buffett reading manuals, and it’s harder now, because holding a view the machine disagrees with means holding a view almost everyone disagrees with, generated confidently, phrased persuasively, and repeated everywhere you look.

The homogenization that creates the opportunity also creates enormous pressure to abandon it.

When every screen, every note, every colleague’s model says the same thing, the contrarian has a particularly tough time.

And a good deal of the time, the contrarian may in fact be wrong, because being different is not the same as being right. That distinction is the whole game, and AI does nothing to make it easier.

So the analytical edge and the behavioral edge may be collapsing into a single thing. The analytical work identifies where the consensus is mechanically narrow. The behavioral discipline is what lets you stand there once you’ve found it.

You need both. You always did.

What’s changed is that the machine may now manufacture the consensus faster and more uniformly than any prior technology, which sharpens the opportunity and, in the same motion, sharpens the discomfort of taking it.

I’ll leave you where I started, which is without a firm conclusion. Maybe AI finishes off the analytical edge for good, and everything I’ve laid out is a rationalization for staying in a game that’s already over.

Or maybe the great homogenizing wave washes out the middle, floods the market with confident, average, identical analysis, and quietly restores a premium to the one thing it cannot produce: An independent mind, willing to look where the machine won’t, and to stay there.

I’d love to hear what you think.

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Toast is a near-perfect specimen. Roughly twelve years ago, three MIT graduates, Steve Fredette, Aman Narang, and Jonathan Grimm, started the company in Aman’s basement in Burlington, Massachusetts. Their first idea failed. They had set out to build a consumer app that let diners skip the agony of waiting for the check, only to discover that the legacy restaurant systems they needed to plug into were too clunky to integrate with anything. So they threw the idea out and did something far more ambitious. They rebuilt the entire restaurant technology stack from the ground up.

When they went looking for money, most investors said no, and not politely. Selling software to small restaurants was, in the prevailing wisdom, a terrible idea. Owners weren’t tech-savvy. Margins were razor-thin. The whole category was a graveyard waiting to happen.

Look at what that “terrible idea” became. Today Toast powers roughly 171,000 locations, up from a single customer in 2013 and up +22% YoY. Over the trailing twelve months, it processed a staggering $204 billion in gross payment volume. Something close to half a percent of total U.S. GDP now flows across its platform.

And this isn’t a tool reserved for corner delis and food trucks, though it serves plenty of those. Toast runs more than half of all Michelin-starred restaurants in the United States, and over a third of all James Beard Award winners. The finest kitchens in the country, the ones obsessed with every gram and every second, chose the same operating system.

So here’s the puzzle that pulled me in. If Toast is this entrenched, this profitable, this dominant in a $1 trillion industry, an industry it has barely begun to penetrate, why was the stock cut roughly in half over the last year, trading close to record-low multiples?

Because that’s where the stock is at as I write this.

Toast traded down around 50% from its all-time high, languishing in the mid-$20s ($14B market cap) while the business underneath it posted record results, grew subscription services and financial technology solutions gross profit 32% YoY, and added a record number of locations.

Arguably, the drawdown wasn’t triggered by anything “breaking.” I believe, it might have been triggered by the market deciding it would rather own something else. And this is where Toast’s situation gets genuinely interesting, because the company has the misfortune of sitting at the intersection of two of the most unloved corners of the market right now. It is part payment processor and part vertical-market software company, and capital has been fleeing both.

Money is being pulled out of fintech and SaaS at a remarkable clip and funneled into anything that can credibly attach the letters “AI” to its story.

Toast, ironically, is quietly building one of the more compelling vertical AI businesses around. The market just hasn’t decided to pay it for that yet. It’s been too busy selling.

That dislocation is the reason I’m writing this. When a high-quality business gets sold off for reasons that have little to do with the business, you get the rare setup every investor claims to want and few actually act on: a chance to buy something excellent while the crowd looks the other way.

Whether that’s what’s happening here, or whether the bears have spotted something real about competition, margins, and a stretched valuation, is exactly what I intend to find out.

I’ll lay out the bull case in five “Bams,” then spend the rest of this deep dive trying to break it. Understanding the business. The moat. Management. The risks. And finally, what it’s all worth.

By the end, you should be able to decide for yourself whether the market is handing you a gift or a warning.

Let’s get into it! (This was another fun one, and I hope you find it as enjoyable)

What this deep dive covers:

• The five-part bull case (”Bam Bam Bam Bam Bam”)

• What went wrong

• The origin story

• The business itself

• Unit economics analysis

• The moat

• The customer

• Is it a good business in a good industry

• The spawner thesis

• Growth drivers and forecasting

• Margins outlook

• Valuation

Disclaimer:

As of the date of publication the author doesn’t own shares in the company; but that may change. 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.

High-Level Thesis: “Bam Bam Bam Bam Bam”-90 Second-Hypothesis

As always, before I get into the weeds, a quick word on how I like to frame these deep dives:

I always come back to Bill Miller’s take on pitching a stock. His point is brutally practical. Portfolio managers have ultra-short attention spans, and as he tells it, there is basically no successful PM he’s ever met who wanted to hear a story longer than 90 seconds. Peter Lynch apparently kept an egg timer on his desk. An analyst walked in, the timer started, and the pitch had to be done in 90 seconds flat. The format Miller recommends is disarmingly simple: here’s the company, here’s the price, here’s the 52-week range, and here are the five reasons it’s a buy. Bam. Bam. Bam. Bam. Bam. Then what it’s worth, why, and the risks.

So that’s the structure I’ll borrow for this opening section. Five pillars. Five reasons Toast (NASDAQ: TOST) deserves a spot on your radar right now, each one a “Bam” that I’ll fire off before going deep on the business later in the post.

One semantic point that matters more than it might seem. I’m deliberately calling this an investment hypothesis rather than a thesis. A thesis is a statement of belief, and the word quietly nudges you toward hunting for information that confirms what you already think. A hypothesis is something you’re actively trying to break.

With that out of the way, let me load the first round.

Bam #1: The Generational Shift from Legacy to Cloud

The restaurant industry is one of the largest sectors of the U.S. economy, somewhere around $1 trillion in annual sales, or roughly 4% of GDP.

And yet it sits near the back of the class when it comes to technology. Most industries spend something like 5% of revenue on IT. Some (significantly) more. Restaurants, according to a 2022 VIC writeup, spend less than 3%, around 2.7%.

Avasant’s annual IT spending and staffing benchmarks study covers over 25 industry sectors, including food and beverage, and continues to confirm this structural gap: IT spending as a percentage of revenue deviates far more by industry sector than by company size or geography.

That gap didn’t appear by accident. For decades, the industry ran on manual paper processes and a fragmented collection of “point solutions” that never really talked to one another, so the under-investment compounded year after year.

The restaurant management software market was valued at about $6.54 billion in 2025 and is projected to grow from $7.49 billion in 2026 to $14.73 billion by 2031, at roughly a 14.5% CAGR, with cloud technology identified as the driver. Critically, cloud solutions accounted for 60.87% of that market’s revenue in 2025 and are growing faster than hybrid and on-premise alternatives.

Picture the setup that dominated for most of that period. Incumbents like Oracle, through its Micros division, and NCR, with its Aloha product, owned the market with on-premise architecture. The hardware lived on dedicated physical servers, frequently parked in a restaurant basement or a sweltering back closet. Those boxes fail. They’re expensive to maintain. And when one goes down, so does a chunk of the operation. Worse, the legacy stack lacked modern APIs, which forced operators into an inefficient patchwork of separate vendors for online ordering, payroll, and inventory. Staff ended up manually re-entering the same data across systems that refused to communicate, a recipe for errors and constant operational friction. The data itself sat trapped in disconnected silos, which meant owners historically made decisions on gut feel rather than anything resembling real-time analytics. A legacy nightmare, basically.

This is the opening Toast walked through. The company is replacing those clunky terminals with a cloud-based “restaurant operating system” meant to act as the central brain of the business.

What makes it more than a generic cloud play is the vertical-specific depth. Toast obsesses over the thousand little things that are specific to restaurants: hardware engineered to survive grease and heat, software that handles the genuinely messy problem of complex menu SKU modifications and ingredient-level inventory. It pulls the front of house (the POS, the handhelds) together with the back of house (kitchen display systems, payroll, supplier management) so data finally flows across the whole ecosystem instead of dying in a silo.

And it’s built for high-stakes conditions. The “Offline Mode” is a good tell here. If the internet drops in the middle of a packed Friday night, the restaurant keeps taking payments and firing orders to the kitchen anyway. For an operator, that reliability isn’t a nice-to-have. It’s the difference between a normal shift and a catastrophe.

Here’s the part that makes this a hypothesis worth the effort, though. The generational shift is still in its early innings. The legacy players still hold close to 50% of the market. They are, in effect, the share donors, and Toast is the primary beneficiary as their increasingly frustrated customers go looking for something modern and flexible.

Consider the runway. For all its growth, Toast today powers only about 13% of U.S. restaurant locations (as of the 2024 Investor Day), roughly 170,000 of them (as of the latest quarter).

Management frames the rest as a vast greenfield, pointing toward a global TAM they peg at something like 15 million locations as international markets eventually march through the same legacy-to-cloud transition the U.S. is living through now.

Whether you take that 15 million figure at face value is a fair question (what % of the TAM is actually obtainable?).

But the direction of travel is hard to argue with.

This is where it gets interesting

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A quick note before you dive in: we recorded this one over video, and at a few points (mainly towards the end) minor connection issues crept in, causing some short cutoffs in the audio. Apologies for the occasional rough patch – the conversation and ideas more than hold up, and I didn’t want a few dropped seconds to keep this one from reaching you.

The heart of the conversation is practical. We walk through a typical research day and where AI actually fits, from idea generation to final conviction – which parts of the process he’s fully automated, which are AI-assisted, and which he deliberately keeps manual. He breaks down the tools he’s most proud of, automating corporate filings, and how genuinely accessible all of this has become for non-technical investors. We also get tactical on prompting: what separates a great investing prompt from a mediocre one, etc., etc.

We close on the harder questions. Where does AI still fall short for serious investment work – hallucinations, bad sourcing, made-up numbers? Can these models do real second- and third-order thinking, or are they pattern-matching plausible reasoning?

If you want to start using AI seriously in your own process, listening to this episode is a must, I believe.

This post is where I go deeper on three more subjects related to TME.

I’ll spend most of my time on the Ximalaya deal, because the more I dug into it, the more I realized how big of a deal this may turn out to be. My first instinct was skepticism. Long-form audio looked, on paper, like a structurally worse business than music streaming, and I went in expecting to talk myself out of liking the acquisition. What I found instead was something more layered. I assess this acquisition from multiple angles.

Before that, I’ll pick up the thread Pitc2d started on the buyback, and untangle the gap between what TME has authorized and what it has actually spent.

And to close, a shorter flag. My friend Niklas raised a point about TME’s 2025 earnings that I think a lot of investors will miss on a first pass, the kind of thing that sits in a footnote, but shouldn’t be missed. I’ll keep that one brief, but I’d argue it’s as important for anyone building a valuation model as the Ximalaya deal. I also added another interesting recent development last-second (the fourth subject we discuss) – a tie-in into Tencent’s ecosystem.

Let’s get into it.

Disclaimer: As of the date of publication, the author owns no shares in the company; but that may change. 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.

#1: $1 Billion Share Buyback Program

When a Chinese tech company announces a billion-dollar buyback, I’ve learned to do two things before getting excited. First, I read the fine print. Then I check whether they actually spend the money. With Tencent Music, both questions turn out to be more interesting than the headline suggests, and the gap between what was announced and what’s been executed says more about the company than the press release ever could.

Let me walk you through the details and my thoughts.

Let’s start with TME’s buyback history: Back in March 2021, in the middle of the brutal selloff in Chinese ADRs, TME’s board authorized a $1 billion repurchase over a twelve-month window. That one came and went. So did a $500 million program announced in March 2023. Over the last five years, TME’s share count is down around 8%.

The one that actually matters today was authorized alongside the Q4 2024 results: a fresh $1 billion authorization running over a 24-month period that began in March 2025. That puts the clock ticking toward roughly March 2027.

“The Company’s board of directors approved an annual cash dividend of approximately US$273 million for the year ended December 31, 2024, and authorized a new Share Repurchase Program up to US$1 billion during a 24-month period commencing from March 2025.”

A board authorization gives management permission to buy up to a dollar amount. It commits them to nothing. Companies announce these things constantly, especially when the stock has been beaten up, precisely because the announcement itself can prop up sentiment at zero immediate cash cost. The real tell however is execution.

So what has TME actually done? In the first quarter of 2025, the company repurchased 5.9 million ADSs for roughly $64.5 million, at an average price of about $10.8 per ADS.

“Under our previously announced share repurchase programs, during the three months ended March 31, 2025, we repurchased a total of 5.9 million ADSs in the open market with cash for an aggregate consideration of approximately US$64.5 million at an average price of US$10.8 per ADS.“

Worth noting: that disclosure referenced “previously announced share repurchase programs” in the plural, so some of that spend may have closed out the tail of the older $500 million program rather than the new one.

After that, TME stopped breaking out quarterly buyback dollars in its non-SEC PRs/earnings releases. You can still triangulate. For full-year 2025, net cash used in financing activities came to about $665 million. That bucket includes the dividend plus repurchases of roughly $57 million; which is just a drop in the bucket compared to the size of the authorized program.

As a result, weighted-average basic ADS barely budged, drifting from about 1,542 million in 2024 to roughly 1,534 million in 2025. That’s a net reduction of only around eight million ADSs. Eight million. On a base north of 1.5 billion. For a company brandishing a billion-dollar authorization, that is a remarkably small dent.

So is it actually accretive?

But then again, during the Q1 call, management said that it intends to actually complete the program.

“… as part of our long-term commitment to shareholder returns, we plan to complete the 2-year stock repurchase program that we announced in March 2025 on time.“

So if we assume the $1 billion program will be executed and that the majority of the “buyback dry powder” hasn’t been used, let’s run the numbers to determine how accretive it might be at the current valuation.

If TME deployed a full $1 billion at recent prices around the $9 mark, you’d retire on the order of 110 million ADSs ($1B ÷ $8.70 share price = ~114.9 million shares), give or take depending on where the stock trades during execution.

Against roughly 1.53 billion ADSs outstanding, that’s a reduction of about 7.5 percent, which translates into an EPS lift in the neighborhood of 7 to 8 percent if net income holds flat. 7-8 percent in EPS growth without requiring any topline growth; that’s far from trivial for a profitable, cash-generative business trading at a dirt-cheap valuation.

Of course, there is some dilution from SBC, and there’s not a full $1 billion left, and the Ximalaya deal (to be discussed next) will also have a dilutive effect, but still, I think this program is very much noteworthy and should be highly value accretive at the current share price.

“Q: And partly relating to that what’s management latest thought on the share repurchase program, we noticed that there’s still a large quota left?

A: Well, we’re still communicating with the regulator on the Ximalaya deal. If there is any update, we will disclose at that point.“ - Q4 Call

The most attractive moment to retire stock is when it’s cheap. TME spent part of early 2025 buying around $10.8 per ADS; the stock has since spent stretches trading below that. If management leans into the program while shares are depressed rather than mechanically spreading purchases evenly, the per-dollar value created climbs. Buybacks executed into weakness are worth far more than buybacks executed into strength.

#2: The Ear Economy – TME Paid Billions for Ximalaya & Whether It’s Worth It

This is where it gets interesting

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These aren’t speculative flyers or trending meme stocks. These are mostly very high-quality businesses that I deeply understand.

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My entire investing philosophy rests on a simple premise: if I can identify these compounders early, wait patiently for the right entry price, and buy them with a comfortable margin of safety, I can later simply sit on my hands for multiple years and let the intrinsic compounding of the business do the heavy lifting for me.

Speaking of simplicity, I recently shared a quote on X that I wrongly attributed to the late, great Charlie Munger:

“If all you ever did was buy high-quality stocks on the 200-week moving average, you would beat the S&P 500 by a large margin over time.”

While I botched the author (I did actually try to verify it at the time of posting and found some confirming evidence; which turned out to be wrong), I still believe the core concept expressed is fundamentally sound, and Munger might actually underwrite it.

Munger was the absolute master of simplicity, and we don’t need to overcomplicate things in order to outperform.

“The reason our ideas haven’t spread faster is they’re too simple.” - Charlie Munger

"Everything should be made as simple as possible, but not simpler." - Albert Einstein

In fact, a friend recently pointed out another incredibly straightforward approach similar to this one to me, and I wanted to share it here as well. He said, if you had a spreadsheet with all the stocks in your investable universe (your watchlist) and added a column to it that simply asks:

Is this (super high-quality) stock currently in a “lost decade”? Yes/No.

And if you’d only ever consider buying these stocks after a lost decade, when no one wants to touch them, but the long-term thesis is still intact – 1-2 transactions a year –, you’d likely crush the market.

When it comes to hunting for true variant perception – finding situations where the market’s current narrative diverges wildly from long-term reality –, when you’re hunting for areas where you could truly find an edge (remember, skill alone is not enough), this column might be a fertile hunting ground.

The logic of this philosophy is simple; yet arguably the hard part is having the absolute discipline to only swing when these outlier opportunities present themselves. Yet, from my experience (me included), most investors have a (very strong) action bias.

There are, of course, a million ways to beat the market (not only quality investing), and I believe successful investing is all about finding a style that matches your psychology.

But perhaps the most elegant and effective way to outperform – and the way that takes the least psychological toll – is to build a high-quality list, track it relentlessly, and wait. Wait for the no-brainers. Then swing. And do nothing. Do nothing for a long time.

When you track over 100 benchmark-beating companies, I can guarantee you that every single year a handful of them will experience a massive drawdown. Sometimes the company is navigating a temporary operational hiccup; sometimes the entire industry is cyclically out of favor; other times, the broader macro market simply tanks. If you can zoom out, look past the short-term noise, and confidently conclude that the core business is intact and the problem is fixable in a reasonable timeframe, that is your cue to swing hard.

“A wishlist allows you to make more informed, well-timed investment decisions. It gives you an advantage because you can capitalize on sudden price drops more quickly than lazier investors who only start their research after the damage is already done.” - from my investment framework

When a market panic hits, you don’t want to start reading a 10-K from scratch. You want to grab your pre-vetted wishlist with stocks you’d love to own at the right price, and immediately see which compounders have dropped into your buy zone. Yes, the Efficient Market Hypothesis claims that a stock’s price always equals its value. And I believe this is a directionally right assessment. However, – and this is where I disagree – while the market is incredibly efficient most of the time, at any single point in time, emotions might cause prices to deviate wildly from intrinsic values.

Additionally, expanding your own watchlist – one stock at a time – and continuously researching different businesses has massive compounding benefits for your own intellect. Warren Buffett famously refers to this as building a “mental database.” By becoming intimately familiar with dozens of different business models and industries today, you radically speed up your ability to analyze similar companies or competitive threats in the future.

Of course, even if you’ve established that 100+ stock mental data base, I believe you should always be open to looking at new ideas. But a 100 high-quality stock universe may be enough to outperform the market. And it’s a great foundation too.

Furthermore, to keep this database sharp, you have to ruthlessly curate it. While putting this post together, I did exactly that: I revised the list and purged what no longer fit.

Finally, your ballpark “margin of safety entry prices” shouldn’t be set in stone. Because new data constantly flows in, a firm’s intrinsic value changes constantly too, meaning you should review and update your target prices periodically – I find an annual review might be the sweet spot. Real business change takes a long time to play out, which means your structural analysis is highly durable, especially for high-quality businesses operating in industries with ideally slow rates of change. Unless a black swan corporate event occurs, revising your ideas once a year will absolutely do the job.

However, this also means you want to cap your watchlist at a certain point, because otherwise this whole process becomes unmanageable.

What We Will Cover in This Post

In this post, I’m going to share the handpicked 150 high-quality stocks in my investable universe (strictly speaking, after some cleanup, I ended up with 148 names). First, I will present the complete watchlist alongside a highly concise, 1-2 sentence breakdown of what each business does and where it operates. Next, we will run a structural analysis on the list – looking at its collective current performance, underlying industry clusters, long-term topline CAGRs, margins, and overall valuation metrics. Finally, we will discuss how to actually put this list to work in your daily routine, going far beyond a lazy “52-week low” screening approach to find true, mispriced value. I hope you enjoy this piece.

Disclaimer: As of the date of publication the author owns shares in some of the names that appear on the watchlist. 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.

My 150-Stock Watchlist + Brief Business Description (& Geographic Focus)

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Now imagine that the market is pricing all of that at roughly 9x forward earnings – and that once you start digging past the headline multiple, the true price you’re paying for the actual streaming engine is even more jaw-dropping than that already-cheap number suggests (to be discussed below).

That is the Tencent Music Entertainment (ticker symbol ) situation today.

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The stock has gone nowhere for over five years. It currently sits 69% below its all-time high. In just the ten months between August 2025 and June 2026, more than 63% of the stock’s value has been wiped out – a collapse that would feel right at home in a speculative biotech bust, not in a cash-generative, monopoly-like platform business beating analyst estimates on both revenue and earnings per share for six consecutive quarters.

So what is going on?

The short answer: the market is reacting to a cocktail of anxieties rather than the underlying business reality. When you layer these concerns (to be discussed below) on top of a persistent “China discount” – VIE structure anxiety, memories of the 2021 regulatory crackdown, rock-bottom Chinese consumer confidence – you get a reflexive sentiment vortex. The street sells first and asks questions later.

The question worth asking, I believe, is: at what point does the geopolitical discount become too much of a discount?

Disclaimer: As of the date of publication the author owns no shares in TME; but that may change. 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 Business of Tencent Music Entertainment Group

Tencent Music Entertainment Group (TME) is listed on both the NYSE under the ticker symbol TME as well as the Hong Kong Stock Exchange under the ticker 1698. TME dominates the Chinese audio landscape through an ecosystem of powerhouse applications like QQ Music and Kugou Music.

Think of it as a much more versatile version of Spotify operating within a highly protective domestic duopoly alongside NetEase Cloud Music. For years, in fact, China’s digital music landscape was highly fragmented and plagued by piracy. The market consolidated rapidly due to two major events:

• Tencent’s Consolidation: In 2016, Tencent merged its own streaming service (QQ Music) with China Music Corporation (owners of Kugou and Kuwo). This brought China’s three largest music apps under one corporate roof (TME), giving them an overwhelming market share.

• The Exit of Competitors: Smaller platforms simply couldn’t keep up with the soaring costs of licensing music; there’s a natural winners-take-most dynamic at play in this industry (think of Spotify’s massive, dominant lead in “the West” (estimated 40% market share) alongside Apple, Amazon and YouTube music). The duopoly became firmly cemented after Alibaba shut down its music streaming platform, Xiami Music, leaving NetEase as Tencent’s only viable, scale-driven competitor.

But back to TME’s business. Rather than relying strictly on the standard freemium streaming model, TME is best thought of as a dual-engine machine that actively pairs a massive digital platform with both sub-licensing and direct content ownership.

Yet Tencent Music Entertainment does not operate as a traditional, singular record label in the same way that a “Major” (like Universal, Sony, or Warner) does. Instead, it employs a hybrid strategy that combines large-scale distribution with an internal production ecosystem.

Its structural integration with Tencent’s WeChat ecosystem provides a virtually unassailable distribution moat, converting casual video and social interactions into sticky, high-margin music subscribers.

“In the competitive world of business, having a superior product isn’t always enough to guarantee success. Often, the ability to distribute a product widely and efficiently can create a significant competitive advantage known as a ‘distribution moat.’ A distribution moat refers to a company’s capacity to leverage its extensive distribution network to dominate a market or quickly penetrate new markets, making it exceedingly difficult for competitors to gain a foothold.“ - from my investing framework

By managing its own in-house music labels and locking down early-release windows for tier-one artists, the business keeps its content procurement costs low while keeping user retention incredibly high.

When you look at how the revenue flows, the business splits its operations into two primary reporting segments:

• Music Related Services (82.5% of Q1 revenue; further broken down into “membership services” (58% of total revenue) and other “music related services”) – formerly known as Online Music – …

• … and Social Entertainment (17.5% of Q1 revenue).

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The Poker Room Paradox: Why Raw Talent Fails at the Wrong Table

To understand this clearly, I often look back to my late teens and early twenties, a period of my life during which I spent a good amount of hours each week playing poker online.

I believe, in poker, the demarcation between skill and edge is brutally transparent – and easier to grasp than in investing, I believe – because it can be quantified with mathematical precision, and if you play at multiple tables simultaneously, sample sizes are large (in contrast to the handful of bets you place in investing).

You measure your performance in big blinds won per one hundred hands played. If you possess superior technical proficiency, your long-term expected value rises.

Consider the "durrrr" Challenge, for instance, which was a legendary high-stakes online poker wager issued in 2009 by Tom "durrrr" Dwan, who offered to play anyone in the world (except Phil Ivey) heads-up for 50,000 hands at $200/$400 stakes, backing his confidence with a massive 3-to-1 side bet where he risked $1.5 million against an opponent's $500,000. It became a defining spectacle of poker history that ultimately ended in incomplete drama: Finnish pro Patrik Antonius conceded the first match in 2010 after falling over $2 million behind, while the second match against Daniel "Jungleman" Cates – who was crushing Dwan for $1.25 million – was abruptly halted by the 2011 "Black Friday" shutdown of US online poker and never formally finished.

I’ve attached the final results and the respective win rates (measured in BB/100 hands) below. For context, in modern heads-up poker, a win rate over 5 BB/100 is considered a significant edge. Both of these challenges saw incredibly high win rates, suggesting that while at the beginning the challenge, both players thought they had an edge on the other, in reality, only one of them had (of course, in a heads up setup; it’s more complex at a six-seat table).

In poker, table selection matters.

You could be the tenth-best Texas Hold’em player on the planet, but if you sit at a table with the nine players who are better than you, you are the sucker.

Throw in the casino rake – the structural cut taken by the platform or house – and a hyper-competitive table will easily turn a world-class player into a break-even grinder.

You simply cannot out-skill a terrible environment.

This exact dynamic governs the global stock market. Think of it through the lens of a classic fishing analogy which Charlie Munger frequently referenced: some bodies of water are teeming with fish, meaning you do not even need fantastic talent to walk away with a heavy catch. Other waters are so heavily fished and depleted that no matter how legendary of an angler you are, you are going to struggle to catch anything at all.

When you can locate an attractive setup (which in itself may be a skill) – a market pocket overflowing with fish – you can exploit that external inefficiency comfortably.

Conversely, when competition is fierce and the “paradox of skill” kicks in, the edge vanishes, the game becomes dictated primarily by luck, and even the elite practitioners begin to flounder.

As Michael Mauboussin famously observed, all the investing skill in the world means absolutely nothing if you cannot find an attractive opportunity to exploit or an easy game to play.

“The key is this idea called the paradox of skill. As people become better at an activity, the difference between the best and the average and the best and the worst becomes much narrower. As people become more skillful, luck becomes more important. That’s precisely what happens in the world of investing. […] That said, over longer periods, skill has a much better chance of shining through.”

– Michael Mauboussin

This brings us to a critical, baseline definition. You possess skills; you do not possess an edge. An edge is an entirely external development, a situational setup or a behavioral inefficiency that appears in the market landscape for you to exploit. It is an event.

But it may also disappear! Think of a broader market panic for instance, where the wisdom of the crowd turns into the madness of the crowd.

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Hence, it is not necessarily a repeatable part of the investment process. You can spot “edge-favoring” setups and lean into them, but they may be temporary.

Security selection skill, on the other hand, is an internal attribute. It is comprised of technical sub-skills like parsing complex financial statements, understanding corporate accounting, assessing competitive dynamics or management integrity, modeling cash flows, and analyzing chosen competitive strategies or incentives embedded in compensation packages. Mastering these disciplines makes you a highly competent analyst.

But while these “sub-skills” are essential, they represent internal capability rather than a market edge, which only manifests when a structural breakdown creates a deep mispricing.

Unless you are a political insider with an uninterrupted, asymmetrical flow of corporate data – a rare, controversial exception that someone like Nancy Pelosi or the Trump family might enjoy – you cannot personally own an edge. It belongs strictly to the environment.

The BAIT Framework & How it Defines Your Counterparty’s Mistakes

To truly exploit an edge, you must transition from broad philosophy to a rigorous taxonomy. If you want to actively look for setups that may present an edge, you have to isolate exactly how these setups manifest in the wild.

This brings us directly to the BAIT framework, a mental model built on the reality that markets are frequently choked with …

• behavioral (B),

• analytical (A),

• informational (I), and

• technical (T)

… inefficiencies.

Before we explain each edge in more detail, let me share some general observations: An edge only exists when you can confidently identify who is on the other side of your trade, while proving with sound theory or empirical evidence why they are wrong and you are right.

If you cannot articulate the counterparty’s specific error, you are the mark at the table (we’re back to the poker analogy).

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In the vocabulary of modern finance, a market inefficiency can be expressed in several distinct ways depending on your level of mathematical rigor. It represents a (positive) expected value that beats the return generated by the broader market. If the market generates a 9% IRR over the next five years, your edge should be measurable by you achieving a higher 5-year IRR. Buffett, the greatest investor of all time, CAGR-ed at roughly 20% over his lifetime (many decades) fwiw. Highly impressive.

But again, the calculation of an “edge” works beautifully in games of defined risks and outcomes, games where you can quickly accumulate large sample sizes, though it becomes significantly cloudier in games of true economic uncertainty and games where you bet infrequently (think of the handful of bets an investor places in a calendar year vs. the 100,000+ of hands a poker player plays in the same time frame) where calculation errors creep in and luck becomes another dominant force.

Culturally, the fancy way to label this phenomenon is a variant perception – a well-grounded view that stands completely apart from the consensus.

Practically, however, an inefficiency is just the gap between current market expectations and structural fundamentals. You could measure this delta using traditional discounted cash flow modeling and reverse DCF analysis to isolate exactly what the market is pricing into an asset today.

There is a critical secondary considerations here that most amateur investors ignore: position sizing. Size matters. In fact, you should never place a bet without a clear edge, regardless of how highly you rate your own internal skill. This is really important to understand, and if anything, this should be your key takeaway from reading this piece.

“Without an edge, one should not bet. Ever. Regardless of skill.“ - Tiho Brkan

Your bet sizing must be dictated exclusively by the magnitude of the external edge you are exploiting. The larger the edge, the larger your capital allocation can be; in theory, up to a mathematically optimal limit. Push past that threshold, and over-betting will inevitably lead to financial ruin.

"Playing poker in the Army and as a young lawyer honed my business skills .. What you have to learn is to fold early when the odds are against you, or if you have a big edge, back it heavily because you don't get a big edge often." - Charlie Munger

And to tie this discussion back to poker again: this is the exact blueprint used in modern poker. In a game that is now effectively solved, a mathematically optimal bet size does in fact exist for every scenario. However, applying this in practice requires a nuance that amateur investors often miss: you cannot simply deploy a single, predictable size all the time. To keep opponents from exploiting, elite players must balance their strategy by thinking in bet-size ranges. You scale your allocation based on the strength of your edge, while remaining mathematically unpredictable to the rest of the table.

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Back to investing … I’d argue that in today’s environment, locating these setups, setups that offer you to lean into and exploit an edge is brutally difficult. Wait for the fat pitches.

“We’re always looking for something where we think we have an insight which gives us a big statistical advantage. And sometimes it comes from psychology, but often it comes from something else. And we only find a few, maybe one or two a year.” - Charlie Munger

So what do the four edges mentioned above actually refer to?

Behavioral Edge

I view the behavioral edge as a direct exploitation of psychological breakdowns in the market landscape. When investors are gripped by collective panic or blind euphoria, they stop acting as cold calculators and begin reacting to raw emotion. Fear reigns supreme. This herd mentality creates an external setup where asset prices dislocate wildly from their actual worth, creating an attractive playground for rational capital. My role here is to let my intensive training in sentiment take over while the crowd makes expensive, emotional mistakes. You cannot force these opportunities. They appear situationally when market participants abandon their senses, hand-delivering a structural mispricing that you can calmly capitalize on if you possess the internal discipline to remain completely rational while others implode.

“Both poker and investing are games of incomplete information. You have a certain set of facts and you are looking for situations where you have an edge, whether the edge is psychological or statistical.” - David Einhorn

Analytical Edge

An analytical edge manifests when you look at the exact same public data as your competitors but arrive at a vastly superior conclusion. This is the art of variant perception, where you articulate a well-grounded view that stands completely apart from the consensus. The consensus is wrong. I actively execute this edge by calculating the stark delta between current market expectations and underlying business fundamentals, utilizing tools like a reverse discounted cash flow analysis to decode what the market is pricing in. It requires deep cognitive skill. You are essentially identifying a massive pricing breakdown because the crowd is misinterpreting a company’s long-term trajectory, allowing you to capture a setup where your unique interpretation of the data aligns with reality while the market remains blind.

“Investors operate with limited funds and limited intelligence; they do not need to know everything. As long as they understand something better than others, they have an edge.” - George Soros

And as Howard Marks very clearly articulates in the quote below, more data or more manpower does not equal better results. Raw scale is a commodity. Anyone with enough capital can hire 160 analysts, buy expensive data terminals, or run massive computing clusters. If everyone has access to the same raw numbers (lack of informational edge) and processes them using standard methodologies (lack of analytical edge), they will arrive at the exact same conclusion. That’s why some fund managers can also ask for multi-million dollar paychecks. They possess unique abilities that allow them to arrive at variant perceptions that turn out to be correct.

"I was recently on a panel that was asked what gave our firms their edge. One panellist responded “we have 160 analysts around the world”. To me, that response demonstrated a total lack of insight. Unless those 160 analysts are more astute than the average investor, they’ll contribute nothing. Certainly another 160 wouldn’t double the managers ability to add value (if they could everyone would be an analyst).” - Howard Marks

Many people argue that finding an analytical edge has become incredibly hard and that this edge may entirely disappear soon.

"There are a lot of trends going on now that make the business very tough. You've had a lot of smart people come into it, making it much harder to find opportunities. You need to determine what your real competitive advantage is." - John Phelan

PS: Funnily, I also came across this quote from Ray Dalio on the website of MastersInvest, and I’m wondering whether Marks was actually referring to Dalio?

“If you’re going to come to the poker table, you’re going to have to beat me. … We have 1500 people who work at Bridgewater. We spend hundreds of millions of dollars on research and so on, we’ve been doing this for 37 years.” - Ray Dalio

PSS: Don’t forget to constantly update your (analytical) view as new information flows in.

Informational Edge

The informational edge is centered entirely on possessing an asymmetric flow of data before anyone else can access it. In an era dominated by instant digital communication, this setup is exceptionally rare for the average investor and can typically only be exploited if you maintain a constant, structural line of insider communication. Data is king. I do not operate in this arena. It is the playground of corporate insiders or political figures – like Nancy Pelosi or the gentlemen referred to in the article below – who leverage unique relationships to feed on actionable corporate data before it ever hits a public screen. While it offers a definitive advantage, it relies on an external pipeline of non-public knowledge rather than superior cognitive processing, making it a high-stakes setup that remains entirely out of reach for most unconstrained market participants who play strictly by the rules.

Technical Edge

A technical edge materializes when structural mechanics force market participants to liquidate assets for reasons completely divorced from intrinsic business value. This is an entirely situational setup driven by rigid institutional mandates, legal rules, fund liquidations, or urgent corporate financing needs.

Think of a massive corporate spin-off – like when Pfizer spun out Zoetis in 2013 – where certain funds receive shares of a newly independent entity but are legally barred from holding stocks without a long listing history or a specific market cap floor. The fund managers are legally forced to dump the stock automatically, ignoring price and long-term fundamentals altogether. This predictable, mechanical selling creates a beautiful breakdown in pricing, allowing unconstrained investors with sufficient patience to step in and capture institutional bargains at a massive discount.

Joel Greenblatt discussed these kinds of setups extensively in his book titled “You Can Be a Stock Market Genius: Uncover the Secret Hiding Places of Stock Market Profits.“

This is a concept Michael Mauboussin integrated into the literature a few years back, and it is a dynamic you can see play out across different asset classes. In real estate, for instance, a forced sale is an involuntary transaction where a property is sold to satisfy debts or resolve legal disputes. Driven by legal orders rather than economic choice, these sales (think of foreclosures or partition actions) often result in selling prices below market value.

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Why “Time” May Be the Only Durable Arbitrage Left

Time arbitrage represents the ultimate external setup. The modern financial ecosystem is systematically, structurally incapable of playing a long game. The short-term arena is a bloodbath of hyper-concentrated talent. Just like that crowded poker table where world-class players trade blows over minuscule advantages, Wall Street pours trillions of dollars and infinite intellectual capital into predicting the next quarter’s earnings or the next Federal Reserve announcement. The participants are extraordinarily skilled.

But when you purposefully extend your investment time horizon, you choose to step away from that hyper-competitive table entirely.

William Browne summarized this reality cleanly: the longer you can extend your time horizon, the less competitive the game becomes.

"The longer you can extend your time horizon, the less competitive the game becomes." – William Browne

This systemic inefficiency persists not because professional investors lack brilliance, but because they lack structural freedom (a technical setup). The entire institutional asset management apparatus is tightly shackled to twelve-month evaluation cycles, tracking errors, and quarterly benchmarks. If a portfolio manager underperforms for two consecutive quarters, clients pull capital, fees dry up, and careers end. Consequently, high portfolio turnover becomes a career survival mechanism rather than an alpha-generation strategy.

“Given the competitiveness of the investment business, we believe it is important in every investment to have an edge, an advantage over the herd. This edge could be a willingness to take a long-term perspective in a short-term-orientated market, a tolerance of complexity when others crave simplicity, or the absence of constraints which either impede the ability of others to act or force them to act in uneconomic ways.” - Seth Klarman

While institutional constraints provide the technical backdrop for this phenomenon, I am convinced that a severe analytical blind spot plays an equally devastating role here. The mathematical reality is that very few investors truly comprehend the non-linear nature of sustained compounding at high, double-digit rates. Our minds are naturally optimized for linear progression – which makes the exponential trajectory of an exceptional business compounding over a decade feel completely counterintuitive. It requires a rare level of cognitive imagination to map out the long-term future trajectories of high-quality, young companies that possess the runway to compound at these speeds. Because the typical analyst spreadsheet truncates abruptly at year three or five, or maaaaybe at year ten, the market systematically fails to price the massive terminal value of these compounding machines. You see peers obsessing over next quarter's margin compression while remaining completely blind to the structural macro-tailwind and resulting trajectory of the business model. This collective lack of imagination ensures that the long end of the equity curve remains mispriced, leaving it wide open for those who can look past the immediate horizon.

Horizon Kinetics explored this market-wide disinterest in long-dated returns, noting that the institutional market systematically and heavily discounts outcomes that lie three to five years into the future. Nick Sleep observed this identical structural flaw during his tenure at the Nomad Investment Partnership, noting that the ever-shortening timeframes adopted by institutional investors mean that longer-term opportunities are typically foregone. Wall Street has willingly abandoned the long end of the curve.

To formalize how this structural vacuum relates to our core success formula, we can look at Richard Grinold’s Fundamental Law of Active Management, …

… which posits that investment success is a product of internal skill multiplied by opportunity.

Success = Skill (+ Luck) x Edge (BAIT)

As an aside, I liked the way the paper begins as it provides some good food for thought on the concept of skill:

“Suppose you could start a new investment management firm. How would you do it? Imagine that your choices even include determining your own skills not so much the level of skill, but that you can choose the “what,” as in “skill at what.” You could be an aggressive stock picker, a growth stock manager, a value-oriented manager, a rotating or cyclical manager, a quantitative manager, an economic sector manager, an index fund manager, or an ex-tended/enhanced index fund manager, to mention just a few possibilities. Which style would you choose?

This paper tries to give some guidance to help you make those decisions. With a little reading between the lines, you can see how these insights might apply to your own investment management operations and allow you to exploit your own particular insights into the market.“ - the first few lines of the paper

If the short-term market is completely flooded with exceptional analytical skill, the net alpha generated there inevitably compresses to zero. The paradox of skill reigns supreme.

If the long-term horizon is completely starved of competition, even a moderate level of technical proficiency paired with a long duration can achieve outsized results.

Time arbitrage is not a separate strategy. It is the operational mechanism that unlocks analytical and technical edges by letting short-term noise dissipate. Think of it as a fisherman finding a pristine, completely unfished lake while your competitors are all tangled up in each other’s lines at a crowded pier.

This dynamic creates a highly predictable topography of return across time. At the front end – spanning weeks and months – the market operates with immense efficiency because high-frequency algorithms, hedge funds, and quant strategies dominate the space. Visibility is clear, and the competition is cutthroat. As you push out to the one-to-three-year mark, catalysts begin to emerge, and sentiment starts to mean-revert. The true structural pricing breakdown happens at the deep end, past the five-year mark. Here, long-term compounding and structural business fundamentals dictate the ultimate outcome, yet almost no one is playing the game. The visual above conceptualizes this as an S-curve, where expected rates of return can skyrocket simply because institutional investors refuse to assume long-term time risk.

Moreover, you must realize that time arbitrage is never free. It carries a brutal psychological toll. John Huber articulated this cost perfectly, writing that the price of gaining this specific edge is the volatility that could occur in the near term. You have to be completely willing to accept the possibility that your stock will go down A LOT before it goes up – even though you should attempt to do everything in your power to time your entry well, of course.

Yet, you will not always be able to get your timing right, and this is precisely why the edge remains so remarkably durable. Wall Street analysts often cannot experience stomach-churning drawdowns. They cannot handle the career risk. They cannot look their clients in the eye and defend a position that is down 30% over a twelve-month stretch, even if the ten-year compounding thesis is ironclad. Their structural constraints prevent them from paying the price of admission.

Ultimately, time arbitrage bridges the entire chasm between internal skill and external edge. Your internal skills – your patience, your emotional discipline, your mastery of reverse DCF modeling, etc., etc., – are the tools you use to survive the journey. The structural short-termism of the institutional market is the external setup that hand-delivers the opportunity occasionally.

In an investing ecosystem that is moving faster every single day, trying to out-trade or out-analyze your competitors on a quarterly horizon is a fool’s errand. You are simply choosing to sit at the hardest poker table in the room.

The only way to win consistently is to change the parameters of the game entirely. Extend your gaze. Embrace the near-term volatility. In a world completely obsessed with the immediate present, a long time horizon is not just a patient virtue. It is potentially the last remaining structural edge.

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Final Thoughts

Applying this framework to the current market environment requires immense discipline. Right now, the broader market is giddy, euphoric, and blindingly greedy. When everyone is chasing the exact same momentum, true behavioral edges in terms of buying opportunities vanish completely – even though they may very well present a behavioral opportunity to exploit when it comes to liquidating positions.

Remember that you cannot manufacture an external setup out of thin air, regardless of your proficiency. If no one told you that a geopolitical crisis was about to disrupt energy markets, you could not make money trading oil stocks.

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Think about an ecosystem so technologically hardened that its core architecture – EVENTIM.Net – can effortlessly handle over 1 million concurrent users during a high-stakes stadium tour on sale for elite artists like Taylor Swift or Ed Sheeran without breaking a sweat, a technological feat very few regional competitors can dream of replicating.

Picture a company so dominant that it has secured the prestigious Arthur Award, widely recognized as the “Oscar” of the global live entertainment industry, in the Best Ticketing Provider category.

And did you know that the 200 major European festivals are owned by just four companies? (the company we cover today being one of them; the other three being Live Nation, Superstruct, and AEG)

Reset! and Live DMA recently published the map above to make the ownership of various music festivals (which historically has been rather obscure to the average consumer) more transparent and accessible to the public.

The company we will cover in this deep dive is CTS Eventim, a business that billionaire founder Klaus-Peter Schulenberg took over as a small Munich-based software firm with just 83 employees back in 1996. Since its initial public offering in February 2000, it has compounded capital to deliver a staggering 2,350% total shareholder return – amounting to a 13% annually over more than two decades (17% before the recent drop).

Yet, if you pull up a stock chart today (the chart above gives a clue), you will confront an entirely different emotional landscape. The equity has been practically dead money, trading completely flat to slightly down over a trailing five-year period. More shockingly, the stock has plunged roughly 50% from its absolute peak achieved only in May last year. This steep correction has caused valuation multiples to heavily compress across the board – be careful just glancing at research terminal multiples though! (for reasons we will discuss below) –, drawing immense skepticism from short-term public market analysts.

So what caused this premier long-term compounder to lose its tune?

The answer lies in a highly visible shift in corporate strategy and near-term execution headwinds, coupled with investors favoring clear “AI winners.”

The company is currently navigating a major physical infrastructure revolution, aggressively transitioning from a pure, asset-light digital software play into a heavy-capital venue powerhouse. It operates iconic spaces like the LANXESS Arena in Cologne – which Pollstar officially ranked as the third most successful entertainment arena on the planet, trailing only Madison Square Garden in New York and the SSE Hydro in Glasgow –, which Eventim has managed since 2012, alongside a rapidly growing international portfolio.

The company here follows PropCo/OpCo philosophy, which is a corporate arrangement that splits a business's real estate assets from its daily operations; PropCo owns the land and buildings, while OpCo leases the property and manages the core business.

“As you rightly mentioned, we follow a propco/opco strategy. We follow the idea and we confirm the idea of an asset-light model for the entire company. Having said this, this is work in progress, but we are in accordance with our planning. I sincerely hope more details can be shared later this year. As I mentioned in the last call and in our bilateral discussions, these are very structured or highly structured discussions and negotiations with a variety of different partners. So please rest assured we are working on this and working on this very hard. Nothing has changed that we want to keep only a minority share in propco. And in other words, we will preserve the asset-light model, but we don’t have a result yet, which can be announced at this state.“ - Q1 Call

Furthermore, it recently celebrated the opening of the Unipol Dome in Milan (the former Milan arena), which stands as Italy’s most modern arena, and secured a major contract to build a brand new 20,000-seat arena in Vienna.

“… after hosting the Olympic Ice Hockey Tournament in February with about 400,000 visitors, the Unipol Dome in Milan opened for music concerts in May this year. The Milan arena contributes, therefore, already to the operational results and is a meaningful addition to our high-margin venue portfolio.“ - Q1 FY26 Call

But this real estate pivot has come at a steep near-term price, hit by unexpected construction cost overruns in Milan that temporarily decimated free cash flow and induced sudden structural panic throughout the market. When CTS Eventim originally took on the project to plan, build, and operate the 16,000-capacity arena for the Milan-Cortina Winter Olympics, the total construction budget was estimated at €180 million. However, a combination of severe global inflation, skyrocketing energy prices, supply chain disruptions, and tight design modifications turned it into a financial nightmare, and the actual cost ballooned to roughly €400 million.

This tension sits at the absolute center of my core investment hypothesis. Is the market right to re-value Eventim as a lower-return, capital-heavy infrastructure play, or is it handing long-term investors a classic mispricing in a structurally competitively advantaged, recession-proof business model? Let’s not forget how this engine behaves during a broader economic crisis. During the 2008–2009 Global Financial Crisis, while traditional industrial sectors faced absolute ruin, Eventim’s ticketing revenues surged by 29%, proving that modern consumers stubbornly prioritize experiences over material goods.

In this comprehensive deep dive, I will break down the underlying mechanics of Eventim’s vertically integrated flywheel, evaluate the hidden risks of its venue expansion, audit its balance sheet strength, and dissect the valuation disconnect to determine whether this live entertainment monopoly represents a compelling asymmetric opportunity for patient, long-term capital.

Disclaimer: I bought shares of CTS Eventim for my parent’s portfolio. 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.

High-Level Thesis: “Bam Bam Bam Bam Bam”-90 Second-Hypothesis

As my regular readers know, if you spend any time presenting investment ideas to portfolio managers, you quickly realize they have ultra-short attention spans. Bill Miller always used to emphasize that no successful fund manager wants to hear a story longer than 90 seconds. Miller’s advice was simple: give them the ticker, the current price, the valuation anchor, and then hit them with five immediate, hard-hitting reasons. Bam. Bam. Bam. Bam. Bam.

That is exactly how I want to look at CTS Eventim. Well, I ended up putting together eight BAMs. Close enough.

Let’s look at the baseline. The stock is currently trading around €56. It has carved out a 52-week range between roughly €49 and €111, pulled down heavily after management issued a cautious outlook in late March that spooked short-term traders.

CTS Eventim’s stock plummeted by up to 21% following its March 26, 2026, annual report because unexpected future headwinds completely overshadowed record-breaking 2025 revenues. The primary shock to investors was a stagnant outlook for 2026, with management guiding for flat-to-minor profit growth that fell drastically short of the increase analysts expected.

“With this in mind, we guide for 2026. In ticketing, we expect all KPIs on previous year level, respectively, slightly higher. In Live Entertainment, we expect moderate growth in adjusted EBITDA and EBIT, whereas the revenues are expected to reach prior year level. Finally, we expect Eventim Group level KPIs on or slightly higher level than previous year. CTS Eventim has a consistent track record of returning value to its shareholders.“ - Q4 Call

This growth slowdown was further compounded by the disclosure of a major structural ticketing contract expiration with Stage Entertainment, leaving a revenue gap the company must now work to fill.

“Finally, let's have a look at our 2026 guidance and our new capital market communication approach. […] Eventim will stay a growth asset for the years to come, even though our 2026 budget needs to compensate for a onetime effect in form of a structural income change from a long-term contract. We have set ourselves ambitious midterm targets in both our segments and on group level.” - Q4 Call

However, treating this as a total loss of the Stage Entertainment relationship misses a critical nuance. On April 20, 2026, CTS Eventim, in fact, announced an extension of their retail ticketing partnership across Germany, France, Spain, and the Netherlands.

So the “structural income change” management warned about in March likely stems from Stage Entertainment insourcing its core, backend white-label software infrastructure. However, Stage Entertainment cannot easily replicate Eventim’s massive consumer-facing distribution network. By maintaining the retail relationship, Eventim protects its high-margin transaction fee volumes on the actual point of sale, mitigating a portion of the feared revenue cliff.

Furthermore, investors were deeply rattled by a massive drain on the company’s liquidity. The final construction costs for the newly opened Unipol Dome in Milan more than doubled from an initial €180 million budget to nearly €400 million, according to a Barclays research note. This aggressive capital expenditure caused full-year free cash flow to drop significantly, and “Cash and cash equivalents decreased by EUR 162,889 thousand compared to 31 December 2024. Cash and cash equivalents decreased, inter alia, due to the higher investments in property, plant and equipment […].“

But if you look past the temporary headwinds and noise, the core compounding machine seems completely intact.

Before listing the core pillars, I want to clarify how I approach this setup. I am intentionally calling this an investment hypothesis rather than a thesis. To me, a thesis implies a static statement of belief, which naturally tempts an investor to search for confirmatory data. A hypothesis is something you actively try to falsify. You test the edges, look for structural cracks, and remain objective. My core hypothesis here is that the market is currently over-indexing on temporary venue cost overruns and a conservative near-term corporate outlook, entirely missing a structural, vertically integrated monopoly that is exporting its elite unit economics globally.

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If you solely track the price developments and financial media headlines, it feels like the party will never end. We’re still dancing.

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But if you look under the hood, things are starting to look incredibly frothy in my view. Traditional valuation metrics – like the Shiller PE ratio or the Buffett Indicator – are already screaming that equities are historically expensive.

The problem? Valuations are notoriously terrible market timers. Markets can stay irrational and expensive a lot longer than most investors can stay patient. Stock market indicators are by no means crystal balls. You cannot use these indicators to predict what is going to happen in a year or two from now.

Let me repeat: They are NOT stock market timing tools!

This is backed up by this research by YCharts that shows that “of |the] seven leading indicators studied, none have consistently predicted major market declines dating back to 1950. Even the most consistent indicators provided a warning signal for only about half of major declines. “ A major decline in this study was defined as a decline of 10% or greater from the S&P 500’s most recent all-time high

So to spot a true cyclical turn, we have to look past standard metrics and focus on alternative, structural, and behavioral signals. I will once again insert my “Market Sentiment Cheat Sheet” below:

In this post, we are going to break down six unorthodox signs that I’m seeing that I believe are pointing to us being in a late-stage bull market.

And maybe more importantly, towards the end of the post, we’ll explore the investor’s paradox in this market regime: why an overextended index can coincide with an attractive environment for disciplined, fundamental stock pickers.

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.

1. The $400 Billion Cash Mountain

The ultimate gauge of market value isn’t a spreadsheet; it’s what the smartest capital allocators in the world do with their money. Right now, Berkshire Hathaway is sitting on an absolutely astonishing cash hoard of around $400 billion as of their latest filings.

Whether you view Warren Buffett (and now CEO Greg Abel) as proficient market timers or simply disciplined buyers, the conclusion is exactly the same: they cannot find anywhere to put this capital to work at reasonable prices.

Aside from a highly surprising, significantly increased stake in Alphabet (Google), …

… and the Monday-announced acquisition of Taylor Morrison for $72.5/share (or an $8.5B enterprise value (premium of roughly 24%)), Berkshire has been hoarding cash, letting their pile climb to vertical, historic records.

When the largest corporate war chest in history refuses to buy because the math no longer makes sense, retail investors should take note. If Berkshire will have a hard time deploying that much capital even during a market crash – Buffett is certainly secretly rooting for a prolonged crash, possibly a lost decade (not another COVID-like flash crash) –, it’s a glaring sign that the broader market has run far ahead of fundamental reality.

2. The Illusion of Market Breadth

When an index hits consecutive records, you naturally want to see a broad army of companies marching upward together.

Right now, that isn’t happening.

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Instead, we have an elite group of mega-cap tech generals pulling up a heavily dragging baseline. A lot has been written about the historic levels of concentration we are witnessing in the S&P right now. The track record of the “top 10” club is generally rather poor. Massive concentration – I believe we’re close to the top ten names of the S&P representing 40% or more right now – will only magnify the impact of this base rate, I believe.

Back to my point: This is what market insiders call poor market breadth or high return dispersion. Poor market breadth occurs when a stock index (like the S&P 500) rises, but only a small handful of heavy-hitting stocks are driving the gains while the majority of the market is stagnant or declining.

Let’s look at the exact breakdown from the S&P 500 data: As of May 64.5% of S&P constituents posted a positive return over the last year. Hovering right at the long-term historical average of 64.1%. Despite a massive rally, over a third of the index is flat or losing money.

The index itself is up 27.6% YoY.

The more significant signal, however, can be found in the chart below (which is a bit dated to be fair!). As of last year, only 39% of S&P members outperformed the index in terms of YoY performance. Strikingly low. Only about 39% of companies were actually beating the index itself back then, meaning the index’s returns are heavily skewed by a few heavyweights.

Given the increasing concentration in the largest index constituents and the “AI winners,” I would not be surprised if the data today doesn’t look all too dissimilar! (feel free to share more up-to-date data in the comments below!)

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This extreme lack of participation is a classic late-cycle indicator. Historically, often when the percentage of outperforming companies dropped into this sub-40% territory, a major market correction followed close behind – most notably ahead of the 1987 crash, the 2000 dot-com peak, the 2020 drop, and the 2025 tariff conundrum.

And if you think about it, this makes sense, right? When a tiny handful of stocks carry the weight of the entire financial system, the index becomes incredibly fragile. If these winners at some point stop “winning,” the index has a problem.

However, this environment completely changes the game for stock pickers. It becomes increasingly harder to outperform the market. As legendary Michael Mauboussin explains in one of his papers:

“Security selection skill is entirely dependent on market dispersion, which is the range of returns across a group of stocks. If all stocks in the S&P 500 return roughly the same amount in a given year, picking the ‘best’ stock yields very little excess return, blunting your skill. High dispersion is an absolute prerequisite for security selection skill to shine.”

3. Global AI Mania

This hyper-concentration isn’t just an American phenomenon. The AI infrastructure boom has officially gone global, turning major international indices into concentrated vehicles moving with the volatility of micro-cap assets.

Look no further than South Korea’s KOSPI index. The index has staged a breathtaking rally, skyrocketing 96.7% year-to-date and climbing 252% from late 2024/early 2025.

Why is an entire national index moving like an early-stage cryptocurrency? The answer lies in its extreme structural weightings. I couldn’t find the actual weight of the KOSPI index, but here are the weightings of the MSCI Korea index:

More than 57% of the entire index is tied up in the fortunes of just two top semiconductor players.

Are you looking for a passive investing vehicle? Look somewhere else!

When you look at the underlying stock returns of some of the biggest and most popular “AI stocks” over the past year, the numbers appear anything by sustainable to me. While the SPY (S&P 500 ETF) put up a strong 29.68% return, look at what the AI hardware and memory supply chains did:

• NVIDIA (NVDA): +51.73%

• Dell Technologies (DELL): +276.05%

• Advanced Micro Devices (AMD): +356.60%

• Intel (INTC): +466.32%

• Samsung Electronics (A005930): +475.01%

• Micron Technology (MU): +905.50%

• SanDisk (SNDK): A mind-numbing +4,291.14%!

These aren’t speculative penny stocks being pumped in internet forums; these are massive, multi-billion-dollar corporations experiencing vertical, parabolic price expansions.

When mega-cap hardware manufacturers trade like speculative lottery tickets, you are looking at classic, textbook market froth.

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4. The Luxury Car Bonus Cycle

Speculative bubbles eventually leak out of brokerage accounts and manifest in the real world. When wealth is generated at a historic pace, consumer behavior shifts rapidly – and we are seeing exactly that happen right now.

In late May 2026, financial media and local reports highlighted an unprecedented phenomenon in South Korea: luxury car dealerships are experiencing a frantic rush of buyers.

The individuals driving this demand aren’t traditional high-net-worth real estate tycoons or seasoned executives; they are rank-and-file chip industry workers from Samsung Electronics and SK Hynix.

Thanks to the global AI chip boom, these employees have been reaping massive industry windfalls, with sky-high corporate performance bonuses scaling into the hundreds of thousands of dollars. According to dealership showrooms, the wealth effect has been instantaneous:

• Dealerships have reported receiving dozens of inquiry calls every single day.

• The vast majority of showroom foot traffic looking at high-end sports cars priced well above 100 million KRW consists of Samsung and SK Hynix workers.

• Google search trends confirm the retail mania, showing a massive regional popularity spike in South Korea for the phrase “Ferrari dealership”.

When middle-management tech employees turn into immediate luxury sports car buyers en masse, this strikes me as another soft sign that we’re in the middle of late-cycle euphoria.

5. CEOs as Cultural Pop Stars

Think about how corporate executives are treated near market bottoms versus market tops. At the depths of a bear market, CEOs are often vilified, hauled before regulatory committees, or blamed for economic downturns. But at the absolute peak of a bubble, some select executives transform into mainstream cultural icons.

NVIDIA’s Jensen Huang has officially achieved full-blown pop-star status. We see a similar phenomenon with Anthropic's Dario Amodei, whose profile has skyrocketed into billionaire, tech-titan status as his company's valuation pushes toward the twelve-figure mark. Even Alphabet’s Sundar Pichai has pulled off a stunning narrative reversal – evolving from an executive facing investor calls for his ouster just a year ago to a celebrated pioneer leading Google's triumphant, multi-billion-user 'agentic AI' era.

Don’t get me wrong: Jensen Huang strikes me as an exceptionally brilliant, humble, and likable leader. But the way the broader public and social media now obsess over his lifestyle (I’m not going to insert the image of him giving a fan a special signature here …) is highly unusual for the leader of a B2B hardware company.

Just take a look at what went viral on platforms like X in late May 2026:

• Mainstream food bloggers tracking him down like a Hollywood celebrity as he casually grabbed pepper buns at a night market in Taipei with his wife.

• Videos flooding financial timelines showing him dancing on stage during an all-employee celebration event in Taiwan.

  

When the general public begins tracking a semiconductor executive’s street-food choices and viral dance moves with the same intensity usually reserved for pop musicians, retail euphoria has peaked. It’s yet another psychological signal that a sector is running on pure, unadulterated adoration rather than sober fundamental analysis.

6. The Trillion-Dollar IPO Rush

The final structural signal of a market peak we will discuss in this piece is the sudden, aggressive rush toward the Initial Public Offering (IPO) market. When market conditions are average, companies list systematically. But when a bubble reaches its maximum thickness, late-stage private companies scramble to go public all at once.

Why? Because insiders, founders, and venture capitalists understand market cycles better than anyone else.

They see the massive premium the public market is currently paying for tech, and they want to capture that peak liquidity before the window slams shut.

Right now, we are witnessing what financial media is calling “The $3 Trillion Reckoning.”

Three of the most highly valued private giants in the world are simultaneously charging toward the public markets:

• OpenAI

• SpaceX

• Anthropic

What makes this absolutely unprecedented is the scale: all three companies are aiming to go public at valuations hovering close to $1 trillion each.

For perspective, there are only a small handful of trillion-dollar public companies in existence today, and these three are aiming to join that elite club all at once. This is not something you are seeing in a normal market environment.

When you see high-profile founders rushing these massive capital raises to market as fast as humanly possible, it’s a major sign the party may come to and end soon.

It’s an implicit admission that they believe raising capital at a trillion-dollar valuation might be a flat impossibility two or three years from now if the macroeconomic environment shifts and the AI bubble deflates.

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The Paradox & Avoiding the “Blast Zone”

All of these signs point to a highly counterintuitive conclusion. If you look at the S&P 500 as a whole, its valuation is stretched so thin that the statistical base rates for 10-year compounded annual returns from these price levels look deeply disappointing.

This macro anxiety was perfectly captured by value investor Matthew Fine on a May 2026 episode of the Value Investing with Legends podcast:

“I’m a simple creature and I’m worried about the elephant in the room. And US equities are such a dominant force in global capital markets today, and the statistical base rates of what somebody should expect from valuations like these are very, very poor. And I’m fearful that there will be a lot of disappointment.

And I personally grapple with how to create distance from the blast zone because [we are] largely a long-only fund, and there will be collateral damage and there are correlations across all equity markets. And I think about how to avoid the fallout from a significant decline in US equity markets.”

So, how exactly do you “create distance from the blast zone” if you aren’t managing a portfolio as massive as Berkshire Hathaway’s and don’t want to sit entirely on a mountain of cash?

You look at exactly where the institutional money isn’t flowing right now.

Because index funds, ETFs, and momentum-driven institutional capital are blindly forcing trillions of dollars into anything loosely associated with AI, they are actively starving the rest of the global market of liquidity.

Arguably, and it sounds almost insane to say this in the current environment, this structural shift has created a paradise – and I’m the first one to admit that “paradise” may be a slight exaggeration, but you get the point – for disciplined, active stock pickers.

While the S&P 500 mega-caps may be approaching a rigid valuation ceiling, beautiful, high-quality, free-cash-flow-generating businesses outside the US and across select emerging markets are trading at rather cheap and compressed valuations, often at record-low multiples.

Pockets of exceptional value are hiding in plain sight. Pockets like e-commerce powerhouse Coupang Holdings, or regional giants like Mercado Libre or Grab Holdings, both of which we recently covered in a deep dive on this blog (check out the Library), are, I believe, worth your attention.

In 2026, payment companies are as unloved as they have been in a decade.

To sum up, I don’t know whether this market cycle turns in two months or two years. But the playbook for intelligent investing remains completely unchanged. Standing aside – potentially raising cash – while the crowd frantically chases trillion-dollar IPOs and semiconductor hype requires serious emotional discipline, but it is exactly how you avoid the eventual fallout. Keep your eyes locked onto true business fundamentals, protect your downside, and remember that the absolute best time to accumulate world-class businesses may be when the rest of the market is too distracted by the retail euphoria to notice them.

What are your thoughts? Are you seeing other unorthodox signs that the market is topping out, or do you think this AI-driven momentum still has room to run? Drop your thoughts and data points in the comments below!

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PS: You might enjoy ’ recent piece on the same subject as well:

Dirtcheapstocks Substack

Calling the Top

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a month ago · 131 likes · 1 comment · Dirtcheapstocks

PSS: You may also enjoy this Barron’s piece (paywalled) —> “If It Walks Like a Bubble and Quacks Like a Bubble, Then It’s Probably a Bubble”

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.

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Ultimately, for active investors, only one metric matters: the total return at the bottom of the statement.

When that number beats the benchmark, we congratulate ourselves on our superior analytical capabilities. When it lags, we blame macroeconomic shifts, temporary market irrationality, or bad luck.

But a portfolio’s absolute return is a deceptive metric. It conflates entirely different types of decisions, hiding structural flaws behind a temporary streak of good fortune, or obscuring brilliant research beneath poor execution.

If you do not know exactly which of your decisions are generating returns and which are destroying capital, your investment strategy is essentially a black box.

To understand where your performance truly comes from, you have to break your process down into its component parts.

Michael Mauboussin has written extensively about the fact that active management relies on just three fundamental levers to drive long-term returns:

• The first is security selection, which is your ability to identify businesses (regardless of quality) mispriced by the market.

• The second is position sizing, which dictates how efficiently you allocate capital across your best ideas based on conviction and expected value.

• The third is market timing, or your capacity to execute transactions at moments when human emotion drives asset prices away from their intrinsic worth.

Most investors assume that if they are decent stockpickers, the rest of the portfolio mechanics will naturally fall into place.

This is a dangerous assumption.

You can be an exceptional analyst who uncovers phenomenal corporate compounding machines, yet still underperform a basic index fund because you systematically underweight your best ideas or trade too frequently.

The human mind is highly adept at creating narratives to justify past behavior, meaning you cannot rely on memory or intuition to evaluate your performance.

Ideally, you need an objective, quantitative audit.

The only way to achieve this clarity is through counterfactual testing. You must compare your actual, real-world portfolio performance against an actual benchmark index (ideally, the highest quality one) or a series of hypothetical, automated baseline portfolios.

Comparing your portfolio against a benchmark like the S&P is well-understood by the investing community and integrated into basically every brokerage account, so I don’t think we need to spend any time on this concept.

When focusing on comparing your performance against a hypothetical alternative portfolio to identify your strengths and weaknesses when it comes to the three aforementioned skills in investing, things get a little more complicated, though.

By stripping away individual variables one at a time, you can (attempt to) isolate the exact financial impact of your selection, sizing, and timing decisions. It is an uncomfortable exercise that forces you to confront your past decision-making, but it is the only way to determine whether you possess a repeatable edge or if you are simply paying unnecessary transaction fees to lag the market.

And most importantly, if you discover you lack skill in a particular area, you can systematically adjust your rules to protect your returns.

Testing Your Pure Security Selection Skill

The foundation of any active investment strategy is the ability to find mispriced assets. Before you can evaluate how much capital to allocate to an idea or when to execute the trade, you must first answer a fundamental question:

Does your analytical framework actually identify businesses that outperform the broader market?

Most investors cannot answer this accurately because their ultimate returns are heavily distorted by erratic position sizes and the exact days they happened to buy or sell.

To isolate your raw analytical capabilities, you have to strip away all portfolio construction and trading decisions entirely.

The diagnostic tool for this is the equal-weighted baseline portfolio. To construct this counterfactual clone, you review your transaction history at the start of a specific tracking period, such as a calendar year. You gather every single security you researched, approved, and ultimately purchased during that timeframe. Instead of using your real-world dollar allocations, you assume that you invested an identical amount of capital into each security on the very first day it entered your portfolio. If you owned twenty stocks over the course of the year, each would receive a strict five percent allocation.

Once this hypothetical portfolio is established, you apply a strict, automated buy-and-hold rule. You do not rebalance when prices fluctuate, you do not add to positions on dips, and you do not trim winners. You simply let the equal-weighted basket run completely untouched until the end of the tracking period.

To calculate your selection skill, you subtract the total return of a relevant market index, like the S&P 500, from the total return of your equal-weighted clone.

If this calculation yields a positive number, your research process seemingly has a demonstrable edge. It proves that your criteria for selecting businesses can beat a passive benchmark when left alone. If the number is negative, it means your stock selection is actively lagging the market.

This is a critical realization. It indicates that the hours you spend reading financial statements, listening to earnings call, analyzing competitive advantages, and forecasting cash flows for your valuation models are not translating into superior asset choices.

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Beyond looking at the individual companies themselves, understanding when genuine selection skills matter more and when it adds less value also requires an understanding of the environment in which you are operating. When there is very little variation between the best-performing and worst-performing stocks (low stock return dispersion), even a brilliant analyst will struggle to generate significant outperformance.

This skill shines in environments characterized by high dispersion, where the gap between winning and losing companies is wide.

Charlie Munger famously noted that the first rule of fishing is to fish where the fish are. No matter how sophisticated your valuation tools or how disciplined your process, casting your line into efficient, highly competitive, low-dispersion areas means you are operating in dead water.

Testing Your Sizing Skill

Once you establish whether your research process can identify outperforming businesses, you must confront the second lever of active management: capital allocation.

Position sizing is the bridge between security analysis and portfolio execution. It is the process of deciding exactly how much of your investable capital to risk on a single idea. In theory, an investor should allocate the most capital to their highest-conviction ideas, where the gap between price and intrinsic value is widest and the probability of a positive outcome (will the market recognize that gap in a reasonable time frame?) is highest.

In practice, however, portfolio construction is often driven by allocation towards assets that provide emotional comfort (often what has worked most recently), historical anchoring, arbitrary percentage limits, or mathematical formulas (like the Kelly formula) that only work if you have a large enough sample size – and even Warren Buffett famously stated that Berkshire Hathaway’s incredible, multi-decade returns are the product of roughly a dozen (about 10 to 12) truly good decisions, averaging out to just one phenomenal call every five years.

To determine if your sizing decisions are actually improving your returns, you must run a direct comparison between your real-world choices and your raw research capability. The baseline for this test is the equal-weighted clone you constructed in the previous step. The counterfactual portfolio is your actual weighted portfolio – you apply the initial or target weights you assigned to them in real life at the start of the period (remember that we want to isolate the sizing decisions from timing elements). Again, crucially, you still maintain a strict buy-and-hold rule for this clone. You do not allow real-time trading, trims, or additions to alter the performance. You are simply letting your chosen weights run.

By comparing these two hypothetical portfolios, you isolate the exact wealth effect of your sizing logic. The mathematical equation is straightforward:

A positive result reveals that your capital allocation framework is functioning correctly. It proves that you successfully concentrated capital in your biggest winners and kept your losing ideas small. If the result is negative, you are suffering from the conviction paradox. This occurs when an investor’s highest-conviction, heavily weighted positions underperform, while the small, tracking positions that they barely funded end up skyrocketing.

If your sizing skill metric is consistently negative, it means your intuition about your own edge when it comes to position sizing is flawed. Your portfolio would have generated superior returns if you had simply closed your eyes and split your capital evenly across every stock you bought.

Facing this data forces you to reconsider how you define conviction. It tells you that your subjective feelings of certainty are actually a contrarian (!) indicator for your performance.

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Testing Your Timing Skill

The third and final lever of active management is timing. In the context of a disciplined investment strategy, this does not necessarily refer to guessing the direction of the macroeconomic cycle (even though it may) or predicting short-term index swings.

Instead, (market) timing represents your explicit trading activity. It is the total impact of your decisions to scale into a position using multiple tranches, trim holdings when they appear temporarily overvalued, trim them when the thesis supposedly changes, add to losing positions during market panics, or hold cash while waiting for the wisdom of the crowd to give way to the madness of the crowd. Thus, it is important to consider your cash allocation, too, when comparing your actual portfolio performance to that of the hypothetical benchmark.

Every time you log into your brokerage account and hit a buy or sell button, you are making a timing decision. Full stop. Most active investors tinker with their portfolios constantly, convinced that these minor adjustments optimize their returns and mitigate risk.

Well, in reality, more often than not, they don’t …

To discover whether this transactional activity is actually adding alpha for you, you must pit your real-world performance against a completely passive version of your own strategy.

The baseline for this diagnostic test is once again the actual-weighted static clone of your portfolio at the beginning of the period you analyze. This clone holds the exact companies you selected, at your exact weights, but assumes you fell asleep on day one of the tracking period and did absolutely nothing until the end of the year.

You compare the performance of this do-nothing portfolio to your actual, real-world portfolio performance influenced by trading decisions in between. Thus, your real portfolio reflects all your frantic adjustments, optimization attempts, your cash balances, and your emotional interventions.

If your real portfolio outperforms the static clone, your execution is superior. Your interventions worked. You successfully bought panic-induced bottoms and trimmed euphoric tops.

If this number is negative, however, your attempts to exploit market inefficiencies were actually just expensive noise. You fell victim to overtrading, panic-selling at the exact wrong moment, trusting your “intuition,” or missing out on massive compounding moves because you were sitting in cash trying to time a pullback.

When analyzing this specific metric, you must also be brutally honest about transaction costs and taxes. A “timing strategy” inherently increases portfolio turnover. If your trading decisions beat the static clone by one percent on paper, but short-term capital gains taxes consume more than one percent of your capital, your net timing skill is destructive to your wealth.

For the vast majority of active investors, this test provides a harsh reality check. It reveals that the desire to do something during periods of market volatility (action bias) is an emotional coping mechanism rather than a sound strategy.

If your static clone consistently outperforms your actual portfolio, the data is telling you to stop trading. Your research is valuable, but your execution is broken. The most profitable adjustment you can make is to automate your process and purposely lock yourself out of your account to let residual compounding do the heavy lifting.

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Time Horizons and the Role of Luck

Running these diagnostic tests on your portfolio will inevitably provide you with hard data, but interpreting that data introduces a massive structural problem:

What’s the right timeframe to evaluate your performance?

Investing is a fundamentally probabilistic field, meaning that short-term results are entirely unreliable indicators of true skill. You can execute a flawed process and win, or you can maintain a flawless process and lose. At least over shorter time frames, that’s entirely possible. We wrote an entire blog post about this idea here:

In short, feedback loops in investing are terrible. That inherent messiness is exactly what makes probabilistic domains so incredibly difficult to navigate – and so utterly fascinating at the same time.

The core issue is sample size. In a casino, a roulette wheel spins thousands of times a day, allowing the house edge to smooth out random variance relatively quickly. In investing, your lifetime sample size is remarkably small. Even a highly active investor might only make a few hundred serious bets over an entire career. More focused, concentrated, long-term investors might only make a few dozen. When you operate with such a limited number of trials, outcome randomness becomes exceptionally difficult to smooth out.

Skill certainly plays a crucial role over a long enough horizon, but luck dominates the short term. While luck dictates your immediate quarterly or annual returns, process-driven thinking and sound probabilistic reasoning are the only things that win out over time.

This reality requires a profound mindset shift that many investors – especially newer ones – struggle to make. Human biology wires us to crave certainty, to seek patterns in noise, and to believe that good outcomes must come from good decisions while bad outcomes must come from bad ones. In a probabilistic environment, that simply is not how the world works. You can make a perfect decision based on thorough research, appropriate sizing, and disciplined timing, and still lose a significant amount of money because an unpredictable, low-probability event occurs.

Conversely, you can make a terrible call based on a hot tip, over-allocate your capital, and get incredibly lucky because a speculative bubble lifts your asset.

To survive as an active manager, you must learn to evaluate the quality of your decision, not just the final result. This is particularly true because short-term stock performance is primarily driven by shifting market sentiment, macroeconomic headlines, or broader industry dynamics. The underlying company fundamentals – the actual earnings growth, cash flow generation, and return on capital that you spent hours researching – rarely dictate the stock price over a six-month or one-year period. Those fundamentals require a much longer timeframe to manifest in the market price.

So if you run the tests we presented in this article over a single calendar year, a sudden sector rotation can make your excellent security selection look incompetent, or your terrible position sizing look brilliant.

Hence, I believe you must track these metrics across …

• a) multi-year periods (I’d suggest 2-4 year time frames) and

• b) rolling periods

… to ensure you are measuring your repeatable edge rather than the temporary whims of luck.

The Hard Choices of an Honest Audit

Conducting such an audit can be a deeply humbling experience. It strips away the comforting narratives we construct to justify our returns and forces us to look directly into a behavioral mirror.

At the end of your tracking period, when you aggregate the metrics from your hypothetical portfolios, you are left with an objective blueprint of your investment capabilities.

This attribution matrix removes all ambiguity, mapping your actions directly to their financial consequences across the three core levers of active management.

The true value of this matrix lies in the actionable clarity it provides. If the data reveals that your security selection skill is strong but your sizing metric is consistently negative, you know your research is solid, but you need to work on your sizing (either managing risk more properly or betting bigger on your best ideas; or both).

If your timing metric is the primary culprit destroying your alpha, the path forward is equally clear: you must lower your portfolio turnover, minimize your trading activity, and intentionally lock yourself out of your account to allow your underlying businesses to compound uninterrupted.

You might be completely in love with the daily intellectual challenge of active management, spending hours parsing financial statements and tracking market dynamics. But if you lack a quantifiable edge in at least one of these three crucial domains, your efforts are effectively a form of expensive entertainment. It is far better to know the truth than to operate in the dark.

Does this mean you need to give up on the pursuit of active investing? Not necessarily!

Remember: Investing is a lifelong pursuit.

Michelangelo’s quote “Ancora imparo“ – which translates to “I am still learning” –, who reportedly spoke these words at the age of 87, stands as a timeless reminder of the value of a growth mindset and a lifelong commitment to curiosity and self-improvement. No matter your age or expertise, there is always room to become better.

If you are currently underperforming, here’s an anecdote I truly enjoy (and you might need to hear this): Sir John Marks Templeton was a famous American-born British investor. He started his investment fund in 1954 when he was 42 years old. His fund underperformed the S&P 500 for the first 14 years. Yet, when he retired in 1991, he achieved one of the most impressive track records of all time (+17,862% over this 37-year period vs. 5,243% of the S&P).

Thus, the irony of long-term outperformance is that you need to have the confidence to stay the course and the willingness to improve and adapt when things do not work out as expected.

Confronting your data allows you to systematically patch the leaks in your process, optimize your true strengths, and protect your long-term wealth.

If the data ultimately reveals that you have no repeatable skill across any of these levers, there is also no shame in acknowledging it. You can always simply pocket your time, buy a low-cost index fund, and let the broader market do the work for you.

Please share additional thoughts or comments you have, and leave a comment!

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Most market participants spend their time operating within the comfortable confines of first-level thinking.

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They ask a straightforward, reactive question, such as …

Is this a good asset?

The answers to that query fill hundreds of generic media headlines, financial blogs, YouTube videos, and podcasts every single day, often culminating in a crowded consensus where everyone stares at the identical set of obvious variables.

“Costco is a high-quality compounder!“

Or what about …

“Adobe is an AI loser!“

If the game were that simple, research analysts and/or historians would easily be the wealthiest individuals on Wall Street instead of working for a fixed salary. As always, Buffett has expressed this view many decades ago already:

"If past history was all that is needed to play the game of money, the richest people would be librarians."

Thus, generating alpha requires more.

True outsized equity returns are born from an entirely different analytical filter. To win, you must step into second-level thinking. You must systematically ask what everyone else is assuming about that stock, what exact expectations are already embedded in the current price – this goes back to the concept of expectations investing by Michael Mauboussin –, and who you are betting against (and why they view the asset the way they do).

And then, when you form a contrarian, non-consensus view, you additionally have to be right!

All/most of the above was shared by Tiho Brkan recently on X in a short, but punchy tweet (all the credits belong to him here), and he wrapped it up beautifully by referencing George Soros: he summarized this beautifully by stating that money is made by discounting the obvious and betting on the unexpected.

I figured this would be a very fitting way to start this post, focused on Wise Plc. The recent dual listing of Wise plc on the Nasdaq on May 11, 2026, alongside its secondary listing on the London Stock Exchange, provides a textbook example of first-level market noise completely blinding participants to a generational asset. When the ticker tape flashed on American screens, the financial media and algorithmic trading desks focused almost exclusively on immediate liquidity dynamics and mechanical price action. As outlined in my recent write-up on Wise’s Nasdaq listing presentation and special call, at first, the stock shot up by 7%, but it quickly encountered structural selling pressure over the subsequent trading days.

Check out my latest piece on Wise here:

First-level commentators instantly pointed to the bleeding tape – which is just a fancy way of saying “the stock is down (big)” – as definitive proof that the entire cross-border payment sector was structurally doomed.

If you only evaluate companies through headline narratives and short-term stock charts, you will completely miss the structural transformation taking place inside this network. They missed it. I have closely tracked Wise’s global infrastructure build since its initial public listing in 2021, and I believe there’s a massive disconnect between current market perception and fundamental operational reality.

This deep operational disconnect is part of my investment thesis. The market is fundamentally mispricing Wise because traditional analysts continue to treat it as a standard, sleek retail remittance application. They see a simple consumer tool and completely fail to comprehend the massive, proprietary payment infrastructure humming quietly beneath the application layer.

My Hypothesis

What I plan to focus on in this article is to outline a bullish hypothesis that I have never seen discussed by anyone following Wise. I’d argue many market observers do actually have a decent grasp on the competitive advantages of Wise and the length of the runway. That’s why if you believe in Wise’s guided underlying EBT margins of 15-20% (keep in mind that Wise is currently “overearning”), Wise isn’t clearly cheap as the implied current Price-to-Earnings ratio would sit somewhere in the range of 30-40x earnings.

This seems particularly “rich” in light of the pressure one can witness in the payments sector and the valuation multiple some “peers” currently demand (to be fair, what Shift4Payments or PayPal really have in common with Wise is simply the industry they operate in).

So the bullish hypothesis I want to discuss stands on two highly contrarian predictions that run directly counter to current Wall Street consensus models (where bears would argue Wise’s very mission of driving take rates to zero will eventually kill its own business).

• First, while the prevailing consensus assumes that Wise, like most businesses, will eventually be entering a permanent growth deceleration phase where revenue trends down (or plateaus in the low teens sooner than later), I predict that we might eventually see top-line growth reaccelerate comfortably above 20% – a pattern you wouldn’t normally find in your Econ 101 textbooks – as its global scale takes effect.

• Second, the consensus believes that Wise’s unique cost-plus pricing strategy naturally limits its ultimate profitability, but in this write-up, I argue that the business might reach a point where it will post long-term profit margins vastly superior to current market assumptions – potentially reaching 40% (or more!) as its massive operating leverage unlocks. Why should Wise not achieve 40% pre-tax margins once it captures a dominant share of the global money movement market? The consensus assumes that aggressive price cuts destroy terminal value, failing to recognize that lowering fees is actually a powerful customer acquisition tool that widens an unassailable structural moat.

Consider the incredible journey of this enterprise. Wise has scaled from absolute zero fifteen years ago to processing a staggering $0.25 trillion – trillion with a “T” – in transaction volume today.

The bull hypothesis is based on the idea that, despite its massive operational expansion, the company has only begun to tap into its total addressable market.

You are looking at a scaling cross-border payments infrastructure monopoly that is being valued like a soon-to-be maturing, legacy money transmitter.

That is the essence of a second-level thinking arbitrage, and it is precisely why the current market setup potentially offers an interesting entry point for disciplined, long-term capital – again, remember, IF (!) that hypothesis proves to be true.

Here’s what we will cover in this analysis:

• Why the post–Nasdaq listing selling pressure represents a classic first–level market trap, hiding a world-class asset behind short–term liquidity noise.

• A granular cost-layer audit isolating Layer 1 variable gross margins from Layer 2 administrative expenses to map out Wise’s latent profitability tier.

• Benchmarking Wise against the automated clearing engine of IBKR to demonstrate how scale economies shared unlock “elite” corporate operating leverage.

• Deconstructing the strategic shift from pure word–of–mouth acquisition to an aggressive 52% year–on–year boost in global brand campaigns.

• Evaluating Wise’s massive structural runway against digital peers – analyzing high–growth, high–fee competitors like Remitly alongside the sheer volume potential proven by Revolut’s 6 billion dollar revenue base.

• Exposing the technical Platform disclosure changes that hide the volume acceleration inside Wise’s B2B enterprise division.

• Tracking the rapid, quiet onboarding cadence of marquee global Tier–1 institutions

• How deep background optimization vectors – such as large language model chat assistants and algorithmic compliance screening – systematically collapse processing costs.

• Wise’s path to becoming a $100B company

Disclaimer: I own Wise shares. 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.

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Last Friday, after another regulatory crackdown that seemingly came out of the blue, the stock plummeted 40% in pre-market trading alongside its close peer, Futu. There was a major intra-day recovery, and UP Fintech ended the day “only” down 25%, but clearly, there are a lot of moving pieces that need to be processed.

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Hence, my goal today is to look directly past the initial panic and analyze the actual structural impact of the latest round of sanctions on Tiger Brokers and Futu. To me it seemed like everyone was simply “selling the headline” because there was maximum fear, and at the time the news dropped, A LOT of uncertainty (especially regarding the size of the fine), …

… but a close examination of the enforcement details reveals that the narrative requires a massive dose of nuance.

There is an ancient Chinese proverb that perfectly captures this exact tension:

“In every crisis, there is an opportunity.”

To find that opportunity, however, you have to bypass the immediate chaos that followed the bombshell news release, and exercise some critical thinking. This is what we are attempting to do in this write-up.

A Word of Caution

Before jumping into the numbers and the actual impact on Tiger and Futu’s businesses, though, I want to be entirely transparent with you. Tiger has been a painful, outright catastrophic investment for me so far, and there is absolutely no way to deny that reality. I’m the first one to admit that!

And if you do not understand what you own, an abrupt 40% drop can leave you feeling sick to your stomach. In fact, when the news broke, I published a detailed thought on X regarding why most retail investors should honestly stick to indexing. Tracking complex, highly scrutinized businesses demands specialized skills, a strict baseline of emotional control, and the ability to think in shifting probabilities.

And even when you behave rationally, you are dealing with a range of outcomes where certain variables remain outside of your control.

First, I believe there is a fundamental temperament problem in retail investing. I consider myself a fairly rational and emotionally stable investor. I played poker for many years when I was younger, which thoroughly trained me to process financial losses without panicking. It forced me to assess outcomes based strictly on the quality of the underlying decision, completely ignoring whether the immediate result was positive or negative. Yet, even with that specific background, I could still feel the heavy, visceral impact of Friday’s 40% drop. When a stock plummets like that – and it happens far more frequently than you may think, as Wix recently demonstrated during one of its many own brutal trading sessions – it is incredibly easy to turn off your rational brain. You let raw emotion take the wheel, and when emotions start controlling your decision-making, the quality of your decisions vanishes.

Second, tracking individual businesses takes serious time and specialized skill. Understanding corporate fundamentals, accurately assessing the quantitative impact of breaking news, and evaluating the embedded expectations in a stock price are attributes you have to actively train. Investing comes with inherent risks. You better understand what you are doing and what you own very well. And even then, there is always a probabilistic range of outcomes and structural macro forces that you simply cannot control.

Also, as some of my followers on X rightfully pointed out, on the spectrum of single stocks you can invest in, Tiger certainly falls into a very unique and inherently more risky bucket. It’s very important to be aware of the games you are playing and the risks you are taking on.

There’s no crying in the casino!

Yet, despite the temporary emotional impact of seeing my portfolio take a heavy blow, I have no intention of selling a single share of Tiger. I am convinced the market overreacted in textbook fashion. At the same time, when I saw a recent tweet from investor Siyu Li, I was immediately forced to look at the mirror and question my own assumptions.

Be Aware of “Endless Thesis Pivoting”

The other day, Siyu Li published a brilliant framework on pattern recognition in portfolio losers, warning against the trap of “endless thesis pivoting.”

The pattern is dangerously familiar: a company misses a core milestone, the stock tanks, and the investor immediately rationalizes the disaster by telling themselves the asset is now crazy cheap, concluding they did not need that specific milestone anyway. A new thesis is born, new hope emerges, and the destructive cycle rinses and repeats.

Siyu may very well be right. In investing, turnarounds seldom turn. The first loss is often the best loss, precisely because unexamined losses have a habit of getting worse and worse over time.

I think it is absolutely critical to keep this counterargument in mind as we evaluate the business of Tiger Brokers. This is a business and investment thesis with a lot of hair on it. And arguably, the thesis has changed from a “growth at a reasonable price” play to more of a special situation case with a lot of regulatory hair on it. Even though I think that’s still not a perfect description of what we are witnessing here, as anyone who owned Tiger and/or Futu, should’ve been very much aware of the hostile stance the CCP has (and had) towards these offshore brokerage firms. This isn’t new at all.

But back to Siyu’s point … Is my insistence on holding Tiger a rational calculation, or am I falling face-first into an endless thesis pivot? That’s something to keep in mind when reading the rest of this post.

So let us dive into the actual context of the crackdown to find out.

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Disclaimer:

I own Tiger Brokers shares. 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.

Contextualizing the Development: A Reality Check

To assess where Tiger Brokers stands today, we must strip away the emotional noise and look at the exact operational and macroeconomic mechanics of the latest regulatory action.

The market panicked on Friday, sending shares of Tiger and Futu down by more than 40% in pre-market trading, because it interpreted the news as a sudden, unpredictable death blow.

However, a systemic review of the enforcement details reveals that this is not an arbitrary corporate execution, but a highly coordinated, macroeconomically driven policy containment.

Here’s my attempt at summarizing the key aspects every investor needs to understand regarding the scope, mechanics, and drivers of this regulatory sweep:

• The Core Action and Penalties: Led by the China Securities Regulatory Commission (CSRC), Chinese authorities announced an aggressive, multi-agency campaign targeting unlicensed cross-border brokerage activities inside the mainland. The regulator named Tiger Brokers, Futu Holdings, and Longbridge Securities, stating intentions to impose severe administrative penalties and confiscate what they classified as “illegal gains” accumulated from domestic and offshore entities.

• The Inter-Agency Coordination: This is not a standalone statement by a single financial regulator. The implementation plan was jointly issued by eight government departments – including the CSRC, the People’s Bank of China (PBOC), the Ministry of Public Security, and the Ministry of Industry and Information Technology – with the formal approval of the State Council. It represents an ironclad, multi-dimensional firewall designed to target the entire cross-border ecosystem, from the digital apps down to domestic banking rails.

• The Two-Year Liquidation Timeline: Regulators have established a definitive two-year transition period to wind down existing non-compliant mainland accounts. During this phased window, affected overseas brokerages are strictly prohibited from processing new buy orders or accepting fresh capital inflows from onshore investors. Onshore clients are legally restricted to one-way sell trades and capital withdrawals to ensure an orderly wind-down without triggering an abrupt liquidity crisis. Following the expiration of this window, these platforms must completely dismantle and shut down their mainland-facing websites, trading applications, and local support servers.

• The Macro Context of Capital Flight: The underlying driver of this heavy-handed intervention is a severe macroeconomic vulnerability. Driven by a lagging domestic stock market and falling fixed-income yields, …

  

  … mainland capital flight accelerated dramatically, with outflows, according to Bloomberg, exceeding 1 trillion dollars in 2025 alone. Onshore residents have aggressively bypassed capital controls using underground money changers, corporate swap agreements, offshore insurance policies, and online brokerages to move liquidity offshore.

  

  I’ll point you to this thread by Brad Setser in the context of this aspect, as I believe he’s adding some important nuance to the figures shared by Bloomberg:

• The Total Scale of Impacted Assets: According to estimates from CITIC Securities, the cumulative investment scale of mainland investors across Futu and Tiger sits between HKD 200 billion and HKD 250 billion (approximately 32 billion US dollars). Out of this total, Futu accounts for the lion’s share at roughly HKD 150 billion to HKD 180 billion, while Tiger represents a smaller footprint of around HKD 45 billion to HKD 50 billion. Given that the daily average turnover of the Hong Kong stock market alone regularly hovers around 260 billion US dollars, the systematic wind-down of these assets over two full years is calculated to have a thoroughly controlled, negligible impact on broader market liquidity.

Going forward, the perimeter has been clearly drawn with zero room for corporate maneuver. Mainland residents seeking global equity exposure will be restricted strictly to highly regulated, state-sanctioned channels such as the Qualified Domestic Institutional Investor (QDII) program, the Cross-boundary Wealth Management Connect, and the established Stock Connect mechanisms.

An Important Lesson on the Signal Value of “Price”

There is a dangerous arrogance that easily creeps into the mind of any investor who spends hundreds of hours reading regulatory filings and building complex valuation models. We tend to fall in love with our own depth of knowledge, gradually convincing ourselves that the market is simply an inefficient collection of panicked retail traders who do not understand the underlying asset.

When a stock collapses, our immediate psychological defense mechanism is to assume that everyone else is acting out of irrational fear while we possess the superior long-term vision.

The trading volume leading up to Friday’s announcement offered a brutal, humbling reality check to that worldview. Many investors, including myself, couldn’t make sense of the price at which Tiger Brokers’ stock traded.

Interestingly, before the regulatory hammer fell, the options market for Tiger began flashing highly anomalous signals. The open interest on the put side expiring over the subsequent two-week window surged to a staggering multiple of over 25 times the call volume. This was not a standard macro hedge. It was a highly concentrated, deliberate positioning by accounts that evidently possessed a structural information advantage.

When you see that level of asymmetry right before a major market-moving event (Tiger’s earnings release on June 2), you have to acknowledge a fundamental truth: inside information and information asymmetry are structural realities of the public markets, and even more so in small caps. The “tape” often registers the corporate reality long before small, independent investors have a chance to access inside information.

This is precisely why we should train ourselves to respect the market’s pricing signals most of the time. Michael Mauboussin, one of the sharpest minds in investment theory, constantly urges investors to ask a simple, clarifying question whenever they think they have found a massive mispricing:

“Who is on the other side of the trade?”

If you believe a stock is absurdly cheap and the sellers are completely mistaken, you are implicitly assuming that the institutional fund managers, algorithmic traders, and connected insiders executing those sell orders are collectively foolish.

And that is simply not true.

Fundamentals alone lose their predictive power when you are operating in an environment defined by severe information asymmetry. If you possess only 90% of the relevant operational information, and that 90% looks incredibly bullish, the absolute valuation numbers on your spreadsheet can lead you into a false sense of security. The critical, often unanswerable question is always what exists inside the missing 10%. In highly regulated, geopolitically sensitive sectors, that missing information is a blind spot that remains entirely invisible until the specific structural risk materializes and hits your portfolio like a freight train. Understanding your own ignorance is far more valuable than pretending you can model a regulatory landscape with absolute certainty.

The hidden trap of deep fundamental analysis is that it frequently morphs into an elaborate exercise in confirmation bias. We conduct massive deep dives, compile exhaustive research notes, and answer hundreds of granular operational questions, only to use that data to justify a pre-determined conclusion that a low valuation represents a safe margin of safety.

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Discussing The Extent of the Fines

When the initial panic gripped the tape, one of the first mental exercises I ran through was a basic probability matrix regarding the scale of the impending fine.

Looking at the sheer violence of the pre-market sell-off, bankruptcy was clearly on the menu of market fears. To be entirely fair to the bears, a terminal outcome was not a zero-probability scenario in the absolute earliest hours of the panic when all we had was the initial statement by the CSRC.

Under China’s statutory legal frameworks for unlicensed financial activities, penalties calculated as multiples of illegal income can theoretically scale up to five or ten times the underlying figures. Furthermore, administrative enforcement can vary drastically depending on whether regulators choose to punish gross revenue or allow a deduction for operational expenses – two doctrines that are actively applied across different domestic legal precedents.

When the exact regulatory details finally materialized later in the day – first revealed by Futu …

and later by Tiger …

… –, the reality turned out to be a more than manageable corporate hit rather than an existential execution.

The CSRC Beijing Bureau issued Tiger a fine of approximately RMB 308 million alongside the confiscation of RMB 103 million in illegal income, translating to a total penalty of roughly 57 million US dollars.

While certainly a substantial chunk of change, this bill represents roughly four months of net income for the 2025 fiscal year, or approximately 10% of Tiger’s total cash reserves. If you isolate their idle, uncommitted corporate cash, the penalty absorbs maybe between 20% and 25% of that buffer.

Yes, it is an expensive parking ticket, but not a death sentence, and my concerns regarding the immediate survivability of the firm evaporated almost instantly.

Games Being Played

The sheer speed of the corporate response tells you everything you need to know about how the regulatory game is actually played in Beijing. Within hours of the public announcement, Tiger published an official statement noting that the company accepted the penalty with absolute sincerity. No corporate legal team reviews a massive, multi-jurisdictional administrative penalty and issues an official corporate surrender on a Friday night unless the terms were negotiated, drafted, and signed months in advance.

This follows standard domestic regulatory theater. The authorities first quietly identify and then plan to contain the structural problem behind closed doors, determine a manageable penalty that establishes a public example without collapsing the underlying enterprise, and then release the news to signal absolute enforcement intolerance to the rest of the market.

In my view, the fine is largely symbolic.

Playing Devil’s Advocate

To play devil’s advocate, it is crucial to address the structural counterargument. Bears will reasonably point out that regardless of whether the fine is “survivable,” it confirms a deeply hostile regulatory stance toward the business model.

Tiger’s services are fundamentally unaligned with Beijing’s current macroeconomic goals of retaining domestic liquidity. Because the founder’s roots, along with the core engineering and research and development teams, remain physically anchored in Beijing, there is no simple way for the corporate entity to fully escape the geographic reach of the state. Never.

Investor Dennis Hong perfectly summarized this reality in a response to one of my updates on X, noting that when investing within the broader Chinese economic ecosystem, you must own what the government actively wants to succeed and fiercely avoid what it has explicitly declared to be socially harmful or strategically threatening.

The state typically signals its policy intent years in advance with immense detail, offering ample warning to exit before the hammer falls. In this specific case, regulators have been explicitly stating since late 2021 and 2022 – and then again in 2025 – that the cross-border activities of Futu and Tiger were operating outside the law. Full stop.

The two slides below shared by reinforce this exact same lens to view China-related stocks with.

You can find Pandawatch’s entire slide deck here:

Pandawatch

Special Edition: Hitchhiker’s Guide to the China Stock Galaxy 2026-2030

Welcome to 2026. Expect another year of volatile China investing narratives…

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6 months ago · 53 likes · 1 comment · Pandawatch

Moreover, we must establish a sharp intellectual boundary between standard regulatory compliance risk and systemic country risk. The market treated Friday’s crash as if it were the ultimate “China Risk” playing out – the terrifying scenario where an entire equity structure is rendered worthless overnight due to geopolitical shifts, Variable Interest Entity (VIE) de-listings, or an escalation over Taiwan.

That’s not what we witnessed on Friday. This was localized regulatory risk that shouldn’t come as completely unexpected to anyone owning these stocks. As mentioned, the CCP signalled its long-term intent well in advance. Anyone who deployed capital into Tiger over the past three years had to know that Beijing disapproved of these platforms servicing mainland retail clients; that reality was thoroughly documented.

The rules were tightened, an enforcement boundary was established, a fine was levied, and a grandfathered transition period was set.

It is an operational hurdle that clears a multi-year cloud of uncertainty, allowing us to finally analyze the business impact based on its true (international) fundamentals.

The full analysis starts here:

The rest of this post covers the actual impact on Tiger Brokerage in six chapters. If you’re serious about sharpening your investing edge, the full post (and all my previous premium content, including valuation spreadsheets, deep dives (e.g. well-known mid- and large caps such as LVMH, Duolingo, Meta, Edenred as well as more hidden gems such as Digital Ocean, Ashtead Technologies, InPost, Timee, and MANY more) and powerful investing frameworks are just a click away. Upgrade your subscription, support my work, and keep learning.

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When Wise executed its highly anticipated dual listing on the Nasdaq, the financial media focused almost entirely on the superficial mechanics of the ticker tape. The opening price hit the screens, algorithmic trading systems adjusted to the new liquidity pools, and during the first few trading days, the share price immediately came under some structural selling pressure.

Short-term traders quickly pointed to the downward price action as proof that the payment sector is doomed and that Wise won’t be the exception spared by the market’s forces, but for those investors who ignore day-to-day market noise, the more insightful story was unfolding elsewhere. While the tape was flashing green (at first Wise’s stock shot up 7%) and red (before later declining for a couple of consecutive days), management was hosting a special call for analysts and owners.

It turned out to be the single most illuminating corporate presentation Wise has delivered, maybe in second place behind the landmark 2025 Owners Day – a high-substance, 120-minute masterclass packed with raw operational data and profound strategic disclosures.

Listening to a call like that requires a completely different analytical filter. If you only look at headline narratives or commentary driven by share price developments, it is easy to move right past the immense fundamental transformations occurring inside this business and the massive opportunity ahead.

“We believe our US listing will help us accelerate our mission, helping to bring more of Wise to everyone in the US, as customers and as owners.” - Company press release

“But in the context of the enormous size of this market, we’re really only starting to scratch the surface. We moved less than 5% of the money moved by people and less than 1% moved in the small business market. So this means that overall, we’re less than 1% of this enormous $43 trillion market of cross-currency transactions. And it also means that there’s a huge opportunity ahead of us. We’ve gone from 0, 15 years ago to now moving $0.25 trillion, but we are building this network and the apps to move trillions.“ - Special call transcript

The presentation was dense with subtle nuances, regulatory engineering details, and competitive dynamics that you can only truly appreciate if you have been tracking Wise’s global infrastructure build for years. And that’s what I’ve been doing ever since the company went public on the LSE in 2021. And the stock hasn’t moved anywhere since fwiw:

It is precisely this gap between superficial commentary and deep, operational reality that makes Wise arguably one of the most misunderstood, high-quality future compounder stocks in the entire global market right now.

To cut through the market skepticism and map out exactly where this business is heading over the next decade, I have broken down the 10 most critical insights from that special call that every long-term investor needs to internalize.

So let’s get right to it!

Disclaimer: I own Wise shares. 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.

Insight #1 - More on the Stablecoin Disruption Risk

As described above, in my view, Wise remains one of the most deeply misunderstood high-quality companies in the global market today. Observers frequently look at the superficial layers of the business – treating it as just another sleek consumer remittance application that will bring take rates to levels where Wise will disrupt its own profit generation capabilities – while completely overlooking the monolithic, proprietary infrastructure humming beneath the surface.

"But if Wise keeps lowering prices, won't they run out of profit?"

Lately, especially in 2025, a new narrative has taken hold among skeptics looking to derail the investment thesis, and that is the supposed disruption risk posed by stablecoins. We have dissected this specific threat on the blog before, and I previously sat down to map out the mechanics of global liquidity in a comprehensive podcast episode with Mike from Money Flow Research. I’ll link all these pieces below:

• Will Stablecoins Kill Wise, Visa, and Mastercard? A Deep Dive into the Real Threat – and the Hype

• From Counter-Positioning to Being Counter-Positioned: Wise, Stablecoins, and the Psychology of Pivoting

• Link to Podcast Episode with Mike from Money Flow Research

The core argument from the bear camp is that blockchain architecture will render intermediaries obsolete by allowing value to transfer instantly across borders for fractions of a cent.

This narrative sounds convincing on paper, but it fundamentally misunderstands how global money movement actually functions. During Wise’s Nasdaq listing presentation, CTO Harsh Sinha directly confronted this crypto-native thesis, offering an analytical reality check that every serious investor needs to internalize.

Moving digital tokens between two crypto wallets is incredibly simple, yes, but the actual bottleneck of global commerce is not the token transfer itself. It is the friction of the real-world perimeter.

The true complexity of an international transaction is concentrated entirely in the on-ramp and off-ramp mechanics. People and businesses do not live, trade, or pay taxes in dollar-pegged digital tokens unless they happen to operate in a tiny, insular web3 sandbox.

As Harsh pointed out during the special call, “people and businesses want to use money in their local form.” They need to pay their local taxes in the United Kingdom or Brazil, settle domestic property transactions, and pay for university tuition.

“The real complexity arises when you actually have to on-ramp off-ramp these table cards to different currencies.”

This means that a stablecoin-based cross-border transaction is rarely a straight line. Instead, it typically requires a complex multi-leg execution sequence, often called a stablecoin sandwich. A user must convert their local fiat currency into a stablecoin, move the stablecoin across a blockchain network, and then convert that stablecoin back into the destination fiat currency via a local exchange or liquidity provider. Each leg of that sequence triggers financial crime screening protocols, compliance tracking, and foreign exchange conversion spreads. When you tally up the total structural friction, the economic argument for crypto rails completely falls apart. Harsh quantified this disparity clearly, noting that “most players in this space using the stablecoin route or stablecoin sandwich would charge anywhere between 100 to 200 basis points or more.”

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Compare that to Wise’s infrastructure, which charges a global average of just 52 basis points, and can drop below 20 basis points for large-volume corridors like Swiss Franc to British Pound transfers.

“… if you were to use stablecoins for doing cross-border payments, we’ve learned that the infrastructure we’ve built is by connecting local payment systems and direct connections that we’ve built, is is delivering a better price and speed than current stablecoin solutions can provide in this space. As we covered before, we charge 52 basis points on average.

And this is actually the global average. In some jurisdictions, if you were to move Swiss francs like CHF 1 million to GBP, we could get it as low as 20 basis points or even lower.”

The reason Wise can routinely outperform blockchain liquidity models is that it connects directly into domestic clearing systems. These massive local payment rails already process trillions of dollars in daily domestic volume, making them highly optimized and remarkably cheap.

Wise simply rides these massive local engines directly, bypassing the expensive intermediary spreads that plague crypto on-ramps.

The secondary argument for stablecoins is their utility as a capital flight hedge in inflationary environments. If you live in an economy where the local currency is rapidly losing purchasing power, holding a dollar-pegged digital asset is a rational defensive maneuver. Harsh acknowledged this macroeconomic reality, highlighting that “the Brazilian real has lost 15% against the USD in the last year,” which naturally drives local demand for foreign-denominated hedges.

Yet, the assumption that stablecoins are the only or best tool for this job is incorrect.

For the vast majority of non-crypto native users, a regulated multi-currency account is a vastly superior proposition. The Wise Account allows users to instantly hold 40 different fiat currencies at the click of a button, swap between them at the real mid-market exchange rate, and actively yield interest on those stable fiat balances without navigating the custody risks, smart contract vulnerabilities, or regulatory gray areas of decentralized finance. The company has already proven its ability to expand this framework into adjacent asset classes, having seamlessly integrated equities and money market funds into the core user interface over the past few years.

If stablecoins eventually achieve regulatory clarity and emerge as an efficient, compliant medium of exchange that users actively demand, Wise will simply add them to the platform as another standard asset class.

“But that said, that is the story today. We will continue to invest and see how this space evolves and as it evolves and if it does solve the problems for cross-commodity was for our customers in some regions, so maybe globally to make it cheaper and faster, we would, of course, use this technology. And the key thing here I want you to take away as the infrastructure we've built is extendable so that it'd be easy to add this.”

The primary takeaway for long-term owners is that Wise is completely agnostic to the underlying technology rail. Their proprietary software layer is entirely extendable. If public blockchain ledgers ever manage to outcompete domestic central bank rails on cost, compliance engineering, and raw speed, Wise will simply plug its global treasury engine directly into those networks.

Until then, their fifteen-year lead in regulatory licensing, domestic payment system access, and global compliance architecture remains an unmatched competitive moat. I am left with the exact same conviction that Harsh shared at the close of his presentation: “looking forward 10 years from now... I see most roads in cross-border payments leading to the Wise Infrastructure.”

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Insight #2 - Wise’s 15-Year Headstart?

When evaluating the global payments landscape, investors routinely crown Visa and Mastercard as the ultimate, untouchable monopolies.

It is an accurate assessment, of course. Two fantastic and incredibly “moaty” businesses. Their entrenched network effects are virtually impossible to dislodge.

Yet, I believe Wise is quietly emerging as another incredibly structurally advantaged, unassailable business in the payments sector – a sector not necessarily known for deep, impenetrable moats (with a few exceptions) – outside of those two card giants.

The source of Wise’s competitive advantage lies in the difficult-to-replicate process of bypassing the traditional correspondent banking system entirely. I think most investors glancing at Wise misunderstand this part.

During the Nasdaq presentation Q&A, David Scharf from Citizens Capital Markets pressed management on this exact topic, asking about the backlog of other remittance providers trying to replicate Wise’s feat of becoming a non-bank member of local domestic payment networks like ACH in the US, BACS in the UK, or Zengin in Japan.

“Wanted to follow up on the competition question. I mean at a high level, it seems like at the end of the day, the biggest differentiation for Wise as you bypass correspondent banks and became a nonbank member of individual payment networks. Can you speak specifically to first, just what are some of the hurdles and challenges for a nonbank to get accepted to a local payment network like ACH, BACS, [ Zengin ] And then secondly, since these payment networks by admitting you have sort of set the precedent for admitting a non-bank, are you aware of any kind of backlog of other remittance providers sort of seeking to ultimately embark on the same kind of integration.“

Founder and CEO Kristo Kaarmann’s response revealed the immense structural headstart Wise has and that younger FinTech upstarts AND legacy providers face. He noted that while Wise has been trailblazing through its first eight direct integrations, “in order to build up this network, it’s kind of not worthwhile doing just 2. You kind of need to invest behind doing the 30 or the 40 or the 50.”

This reality completely changes how we think about competition. Gaining direct access to a central bank clearing system as a non-bank financial institution is a regulatory and technical nightmare. It requires YEARS of intense local lobbying, securing bespoke financial licenses, maintaining pristine compliance frameworks, and embedding custom physical and digital infrastructure directly into domestic clearing houses. Doing this in a single country takes years of focused effort. To build a globally competitive cross-border network, a competitor cannot just succeed in Japan or the UK – they have to repeat this agonizing process dozens of times across the globe.

And importantly, as Kristo bluntly observed, “none has seriously started yet. So it’s hard for me to comment how far behind they will be. But it’s an enormous investment of time, efforts and the kind of expertise that you need to build up.” If no one “has seriously started yet,” Wise, which was founded in London in January 2011, has a 15-year headstart!

Insight #3 - On the SES Playbook

This fifteen-year operational lead is further reinforced by an exceptional economic model and corporate DNA known as the scale economies shared (SES) framework.

In a standard business model, growing transaction volumes should yield fatter profit margins as operating leverage dynamics are harvested that are promptly pocketed by executives and shareholders.

But that’s a rather vulnerable, and arguably short-sighted model to run a payments business as PayPal has learned the hard way. For years, PayPal’s branded checkout button commanded premium take rates – historically hovering around 2.9% plus $0.30 per transaction. This yielded strong margins because merchants paid a premium for PayPal’s trusted brand and built-in buyer base. However, aggressive unbranded competitors like Adyen and Stripe disrupted this dynamic. Instead of relying on a consumer-facing button, these processors embed directly into a merchant’s white-label checkout, driving massive volume by undercutting PayPal on price. Unbranded processing operates on razor-thin margins, often charging large enterprise merchants take rates well below 2%, and sometimes just basis points above interchange fees. To defend its market share, PayPal had to aggressively push its own unbranded solution, Braintree. But this strategic pivot forces a brutal trade-off: trading its high-margin branded volume for low-margin unbranded volume, effectively neutralizing the very operating leverage that executives and shareholders once took for granted.

And here comes Wise! Because Wise flips this script entirely.

They treat their massive infrastructure as a utility, systematically returning the cost savings achieved through higher volumes directly back to the customer in the form of lower fees and faster speeds.

This creates an agonizing dilemma for any potential rival trying to catch up. If a well-capitalized competitor decides to spend the next decade and hundreds of millions of dollars building direct integrations into global payment systems, they will eventually emerge into the light only to realize that Wise has already used its massive scale to cut global average prices down to 52 basis points or lower. The competitor, lacking Wise’s multi-billion dollar volume and the underlying infrastructure, regulatory licenses, etc., etc., to subsidize their fixed operational overhead, would have to price their transactions at a devastating loss just to match Wise on day one. Wise has effectively commercialized its cost structure into an aggressive defensive weapon, starving out potential rivals before they can even achieve the scale and the infrastructure buildout required to compete.

When you blend this scale economies shared flywheel with the sheer operational friction of global regulatory licensing, it becomes incredibly difficult to envision any traditional competitor ever catching up.

The structural moat is simply too wide. Barring a radical, black-swan technological regime shift – such as the stablecoin disruption risk we already debunked – Wise’s position at the center of global cross-border rails looks increasingly bulletproof.

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Insight #4 - Measuring the Scale Economies Shared Playbook: Nick Sleep’s Formula

To truly appreciate the strength of this operational framework, we might look past vague corporate buzzwords and actually measure the financial footprint of Wise’s economic engine.

Most of my readers are likely familiar with Nick Sleep, who famously generated a 921% return over 13 years at Nomad Investment Partnership by mastering a counter-intuitive concept: the aforementioned idea of scale economies being shared.

To quantify this dynamic, Sleep invented the robustness ratio, a metric designed to calculate exactly how much economic value a company passes back to its customers relative to the residual profit it retains for its own shareholders.

When Sleep ran these calculations on Costco, he discovered the wholesale retailer operated at a ratio of roughly 5:1. For every single dollar Costco kept in profit, it left five dollars in savings inside the consumer’s wallet compared to standard supermarket prices.

Costco is, in fact, considered the gold standard of scale economies shared.

Yet, Wise is weaponizing this exact formula today at an even higher clip, a reality made clearly measurable by the fresh financial disclosures from their recent Nasdaq listing.

When I first ran these calculations on Wise two years ago using their fiscal year 2023 disclosures, the results were staggering. Back then, the network saved its cross-border users £1.5 billion while retaining just £114 million in net income, yielding a jaw-dropping robustness ratio of 13.2. It was a textbook manifestation of a management team aggressively prioritizing multi-decade customer goodwill and terminal market share over short-term earnings optimization.

Updating the math with the preliminary fiscal year 2026 data allows us to see how that structural defensive perimeter is tracking. According to the listing presentation, Wise generated $3.3 billion in direct customer savings compared to traditional market alternatives.

Meanwhile, the business pulled in $2.5 billion in preliminary total net revenue. If we apply a net income margin of roughly 18% based on their first-half financial profile, Wise generated an estimated $450 million in full-year net profit for its equity owners.

Placing those updated metrics into the formula – dividing the $3.3 billion in quantified customer savings by the estimated $450 million in total shareholder profit – leaves Wise with a current aggregate robustness ratio of 7.33.

On a superficial level, an analyst might glance at this shift from 13.2 down to 7.33 and assume the company’s customer centricity is beginning to erode. That conclusion would be entirely incorrect, as it overlooks a massive structural evolution in the revenue mix.

Back in the 2023 fiscal year, cross-border payments was the undisputed primary engine of the firm (around 70% of total revenue). Fast forward to today, and core cross-border transaction fees have stepped down to comprise just 52% of the total revenue mix. The remaining 48% is now driven by the rapid scaling of adjacent ecosystem products, specifically international card services and net interest income from customer balances.

This product diversification means that looking at the aggregate company-wide net income distorts the true underlying efficiency of the payment rails. The $3.3 billion in customer savings highlighted by management refers specifically to the efficiencies achieved on the cross-border transaction side of the house. If we isolate that specific segment, assuming a standard 18% profit margin on cross-border operations and halving the retained net income to align with its 52% revenue contribution, the true transaction-level robustness ratio remains comfortably in the exact same double-digit territory as before.

Even if you choose to take the more conservative, consolidated company-wide ratio of 7.33, the underlying economic reality remains an absolute powerhouse. It demonstrates that for every single dollar Wise keeps in net profit across its entire global enterprise, it actively leaves more than seven dollars (or up to $14) in its users’ pockets.

The best part is just ahead — unlock the full post to continue (insights #5-10).

Investing is the one domain where better thinking compounds. If this intro gave you new insights, the full piece goes even deeper. A paid subscription is an investment in your decision-making process – and your returns.

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Sure, Mercado Libre is also facilitating transactions, but on top of this, it has built, and still is in the process of building, an entire digital economy through its synergistic pillars: the marketplace itself, the fintech platform Mercado Pago, the logistics network Mercado Envios, and the advertising arm Mercado Ads.

Together, these elements create a self-reinforcing flywheel that few companies in emerging markets can rival.

What fascinates me most is the sheer runway for growth. E-commerce penetration in Latin America still lags the US, UK, and China by roughly half, while a vast swath of the population remains underbanked or unbanked. Mercado Libre has positioned itself to capture both markets. Its fintech arm, Mercado Pago, leverages proprietary data and machine learning to extend credit to underserved populations – replacing the need for traditional collateral and turning users’ digital footprints into financial access. Meanwhile, the logistics network now reaches over 94% of the population in key markets, enabling deliveries that brick-and-mortar retailers struggle to match.

Moreover, Mercado Libre’s management doesn’t chase short-term profits. Their “time arbitrage” strategy is clear: invest boldly now to cement long-term dominance. The mantra “The best is yet to come” isn’t marketing fluff – it’s a strategic declaration.

This approach has allowed MELI to compound value at an extraordinary pace. Even with the current 37% drawdown and a stock that has essentially gone sideways over the past five years, …

… it’s remarkable to remember that over nearly twenty years, Mercado Libre has delivered an average annual return north of 24%, far outpacing global indices.

Yet dominance has its challenges. Chinese entrants like Temu and Shein are pressing into Brazil and Mexico with aggressive pricing and logistics models. Singapore-based Sea Limited's Shopee is also actively competing with Mercado Libre in Brazil. Furthermore, regulatory scrutiny is intensifying in key markets, particularly around anti-competitive practices and data governance. The platform, too, faces reputational pressure from counterfeit products and disputes over seller accountability.

Still, Mercado Libre’s network effects, data-driven lending, and logistical moat give it a resiliency few competitors can match, even in the face of Latin America’s macroeconomic volatility.

Did I catch your interest yet? This was only the start, as this post is a deep dive into Mercado Libre – not just as a stock, but as a business, a strategy, and a regional powerhouse. I’ll explore how it built its moat, how management thinks about growth, the risks it faces, and ultimately, whether the stock still deserves a place in a long-term portfolio.

Disclaimer: I own Meli shares. 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.

High-Level Thesis: “Bam Bam Bam Bam Bam”-90 Second-Hypothesis

As always, let’s return to Bill Miller, who has long emphasized that portfolio managers have ultra-short attention spans. He recalls how Peter Lynch would literally set an egg timer for 90 seconds during stock pitches – forcing the analyst to get to the point fast. As Miller put it: “The best thing to say is, ‘I want to talk to you about [stock]. It is at $X. Here are the five reasons why I think it is worth $Y.’”

Following that philosophy, I want to give you the quick Mercado Libre hypothesis. Admittedly, this one is longer, and I’m playing around with different formats here. I went with a similar format in my Nintendo write-up, where I try to condense down the core thesis to the most important pillars (I ended up putting together eight “BAMs” in this write-up; but one could certainly add more), and then couple this with a very thorough business model overview, thoughts valuation, plus other relevant findings.

Mercado Libre (Ticker: MELI) is trading around $1,556 as I type this, down another 4-5% after the 10% decline post Q1 results, as highlighted, even as net revenue growth reaccelerates to 49% (its fastest growth in four years). I believe it might be an interesting buy for the following five reasons:

• Bam 1 – Ecosystem Control: It controls Latin America’s leading integrated commerce and fintech ecosystem, creating a self-reinforcing flywheel where marketplace traffic feeds payments, and payments feed deposits and proprietary credit data, and traffic feeds its advertising business, and the generated profits are reinvested into its infrastructure and logitiscs.

  “Mercado Libre’s logistics network is one of our strongest competitive advantages. We deliver a world-class experience for buyers, driving greater traffic, conversion, and sales for sellers, along with higher NPS and improved cost efficiency. We continue to innovate and offer multiple delivery options, such as Next Day Delivery, Meli Delivery Day, and Meli Places“ - Company website

  

  

• Bam 2 – Riding the Secular Growth Runway: One of the most compelling reasons to consider Mercado Libre is the sheer size of the opportunity in Latin America. While e-commerce is a global trend, the region remains roughly half as penetrated as more mature markets like the U.S., UK, or China.

  

  Regional e-commerce penetration sits in the mid-teens as a share of total retail, compared with roughly 15-25% in the U.S. (depending on which analysis you look at) and over 35% in some Asian countries. Management believes the market could more than double – in fact, LatAm e-commerce has been the fastest growing market recently – as the region catches up, meaning the growth runway is enormous.

  

  The habit of online shopping itself is still in its infancy. The average American makes 41 online purchases per year; in Latin America, that number is just seven.

  “We have a once-in-a-generation opportunity to transform how hundreds of millions of Latin Americans shop, pay and access financial services. In commerce, the region is at an early stage of a shift that markets like the US are much further along. The average American makes 41 online purchases per year whereas the average Latin American makes just 7. Our own buyers average 11 – ahead of the region, but still a fraction of what we expect over time as online shopping becomes a habit for many more people.“ - Q1 Letter

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• Deep Dive: Tiger Brokers ($TIGR)

• Revisiting UP Fintech ($TIGR)

• 2026-01: Investing in China When the Rules Are Changing: Opportunity, Illusion, and Tail Risk

• The Most Absurd Stock Valuation in the Market Right Now?

• Robinhood’s Entry into Singapore: Assessing the Competitive Threat to Tiger Brokers and Futu

Despite all the noise surrounding anything China-related, and despite the underwhelming share price performance, I still believe it is one of the most inefficiently priced stocks in all of Asia right now.

Most investors see a volatile fintech play. The “Robinhood of Asia,” which completely misrepresents the business we are looking at.

I see something else entirely. A brief discussion in our community this morning sparked a new perspective for me, which I wanted to write about.

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Tiger being a prime takeover target at a roughly $1B market cap has crossed my mind before. But today, for some reason, I was immediately reminded of a valuation framework I applied to Edenred recently (in the article linked below; that buy worked out quite well from those levels so far).

In that analysis, I argued the market was completely ignoring the replacement cost of the underlying network (merchants, users, corporate clients).

It turns out that when you apply that same logic to Tiger, the math becomes similarly impossible to ignore.

This is not a traditional discounted cash flow exercise. It is a look at what the business is actually worth to a strategic buyer today. It’s another lens to illustrate the massive mispricing in Tiger’s stock.

If a massive financial institution wanted to replicate Tiger’s footprint in Singapore or Hong Kong, they couldn’t just snap their fingers. They would have to spend a lot of money (and I’ll share my best estimate in this analysis). They would have to fight for every single user.

This replacement cost lens provides a massive, invisible floor for the stock that the current market price seems to completely disregard. I also looked at churn and valued the Up Fintech funded account base on an LTV basis. This was fun. I hope you enjoy the analysis.

Disclaimer: I own a lot of UP Fintech stock (relative to the rest of my portfolio). 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.

If you’re unsure about what you get, here’s an overview of some of the companies we covered in a little over a year:

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The acquisitions of foodpanda Taiwan and Stash Financial were both announced in Q1 2026, but if you check the latest Q1 earnings, you won’t see these deals reflected anywhere in revenue yet.

Both are still awaiting regulatory approvals, and Grab categorizes them as “planned acquisitions” in the earnings report; and thus, they are also not reflected yet in the full-year 2026 revenue guidance of $4.04B – $4.10B.

At first glance, it’s tempting to ignore these deals when thinking about Grab’s valuation. After all, the revenue hasn’t hit the books, and the balance sheet hasn’t felt the full impact – and what if they won’t be approved by regulators in the first place? In 2025, Uber ended its ambitions of acquiring Delivery Hero’s foodpanda in Taiwan, roughly three months after Taiwan’s antitrust regulator blocked the deal (citing competitive issues).

But that would miss the bigger picture. As investors, we are required to project the future, and each acquisition adds a meaningful slice of revenue and, in the case of Stash, a high-margin, subscription-based business that could reshape how to think about Grab’s overall revenue, margin, and growth profile.

The numbers tell part of the story. Foodpanda Taiwan closed at $600 million in cash and delivered roughly $1.8 billion in 2025 GMV.

Stash, with a 50.1% stake priced at $425 million, manages over $4 billion in assets under management.

Both deals are sizable, but the more interesting question is what they contribute to Grab’s future revenue once fully integrated.

This is what I was thinking about over the past weekend, and I put together this write-up. Because by ignoring these acquisitions, the market could be underestimating the scale and quality of revenue that’s about to hit the company – very soon!

In the sections that follow, I’ll break down the projected revenue contributions, how deal-related cash outflows affect Enterprise Value estimates, and how to think about Grab’s valuation after making some key adjustments.

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The full story starts here:

The rest of this post covers TAM penetration and market share dynamics, the identification of key growth drivers going forward, and two valuation approaches. If you’re serious about sharpening your investing edge, the full post (and all my previous premium content, including valuation spreadsheets, deep dives (e.g. well-known mid- and large caps such as LVMH, Duolingo, Meta, Edenred as well as more hidden gems such as Tiger Brokers, Digital Ocean, Ashtead Technologies, InPost, Timee, and MANY more) and powerful investing frameworks are just a click away. Upgrade your subscription, support my work, and keep learning.

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Imagine two Harvard Business School students, sitting together, brainstorming ideas that would go on to reshape the landscape of Southeast Asia’s transportation, food delivery, and financial services.

In 2012, Grab was born from a simple, but ambitious business plan aimed at tackling one of the most unreliable and unsafe taxi systems in the world – Malaysia’s. Fast forward to today, and Grab has evolved from that small idea into effectively the “operating system” for daily life in (most of) Southeast Asia.

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And Grab is not just another app. In Southeast Asia, Grab is more than a name – it’s become a verb for many. People say, “It’s raining; let’s just Grab to the mall instead of walking” or “I’m too tired to cook, should we Grab some Thai food?” without thinking twice. And it doesn’t stop there – Grab has mastered the art of creating habits, seamlessly integrating itself into the daily lives of millions of people who now rely on it for everything from getting from point A to point B to paying bills to online shopping.

Grab’s story is also a survival narrative that most tech companies would envy. In 2018, Grab managed to outmaneuver the global giant Uber in its home region and, eventually, when Uber basically gave up, acquired Uber’s Southeast Asian operations in a unique deal. That was a pivotal moment in Grab’s history, solidifying Grab’s dominance and ensuring that Uber’s exit from Southeast Asia would make Grab the undisputed leader in ride-hailing and delivery services.

Fast forward to 2025, and Grab hit a major milestone: profitability. After years of burning through capital in pursuit of growth, Grab reached its first full year of net profit in 2025 – $200 million in the black. This marks a significant inflection point, where the company is transforming from a “growth-at-all-costs” startup-like-business into a more disciplined, profitable growth powerhouse with a clear path to long-term value creation.

But here’s the thing: Grab’s story is far from over. In fact, despite its massive scale already – Grab is a multi-billion-dollar company today –, the company is still just scratching the surface. Grab’s Monthly Transacting Users (MTUs) currently represent just 6% to 7% of the Southeast Asian population. That means there’s a huge growth runway ahead – roughly 94% of the market is still untapped.

What’s even more exciting is Grab’s ecosystem flywheel. About two-thirds of Grab’s users engage with two or more services, and for those using three or more services, the one-year retention rate is an impressive 88%. When customers stay, they not only use more services, but they spend more, too. This is a growth driver that keeps feeding itself.

And then there’s Grab’s cutting-edge use of AI-driven efficiency. By utilizing proprietary AI models, Grab has been able to double its revenue from 2022 to 2024, all while keeping headcount flat. Over 90% of mobility rides are now dispatched using AI, showcasing just how much the company has automated its operations.

On the financial side, Grab has successfully expanded into digital banking. In just three years, Grab amassed 7.4 million deposit customers across its three digital banks, and its loan portfolio crossed the $1 billion mark in 2025 and is expected to exceed $2 billion by 2026 year-end.

And as if all of this wasn’t enough, Grab is setting its sights on global expansion. In early 2026, it completed the $600 million acquisition of foodpanda Taiwan, signaling the company’s intention to push beyond Southeast Asia and tap into new, high-value markets. This marks the first major step in Grab’s journey toward becoming a truly global player.

Of course, this is just the tip of the iceberg. Grab is positioning itself for the future with ambitious plans around autonomous vehicles (AVs) and scaling its financial services. The company is constantly innovating, finding new ways to integrate its services and make the lives of its users easier and more connected.

But what does this mean for investors? Can Grab sustain its growth? What challenges lie ahead? And most importantly, what kind of valuation can you expect as this story unfolds?

As you can tell, Grab is a fascinating business to dive into. So let’s do exactly that and dive deeper into Grab’s history, business model, management team, moat stack, its future, and the growth drivers that will continue to propel this Southeast Asian juggernaut forward.

Are you excited yet? Let’s dive into the first part (11,500 words) of this deep dive.

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Disclaimer: I own Grab shares. 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.

High-Level Thesis: “Bam Bam Bam Bam Bam”-90 Second-Hypothesis

As always, in the style of legendary investors like Bill Miller and Peter Lynch, I’m attempting to give you the quick 90-second rundown on Grab Holdings (GRAB) – what’s the investment hypothesis (which we are then going to test throughout this deep dive?

Right now, the stock trades around $3.6, a significant pullback from its 2021 debut. While that might sound concerning, the real story lies in the business transformation that’s been happening under the hood (i.e. not reflected in the share price).

Here are the five reasons why I think Grab is an interesting opportunity:

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In my latest video, I dive into this tension. Inspired by a recent Goldman Sachs podcast featuring their quant team, I explore the widening gap between how machines “price” assets and how long-term value is actually built.

The big question I want to pose to you: Is AI making markets more efficient, or is it just making them more reactive?

I’ve shared my full thoughts in the video, but I’d love to hear your take in the comments – are you finding AI to be a net positive for your strategy, or is it just adding noise?

Why join the community, you may ask? Our library is fast approaching 70 comprehensive deep dives, providing institutional-level research on some of the world’s most fascinating businesses. Most recently, we’ve dissected companies like Grab Holdings ($GRAB), Fair Isaac ($FICO), and Topicus.com ($TOI).

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• InPost ($INPST) - generated an IRR of >400% as an acquisition offer emerged 2-3 months post write-up

• DigitalOcean ($DOCN) - up 5x in less than a year post write-up release

In my view, the primary job of an investor is not to find reasons to buy, but rather to find every possible reason to say no.

The investment landscape is one where the default state is noise – an endless stream of pitches, charts, headlines, CNBC interviews, engagement-bait on social media platforms, and quarterly updates designed to make you feel like you are missing the next great multibagger.

In my experience, the only way to survive this onslaught without diluting your capital into mediocrity is to develop a set of filters so rigid and so uncompromising that they feel almost violent.

The Violent Art of Saying No

I spend the vast majority of my time looking for the exit door on a research project. If I can find one structural flaw, one misaligned incentive, or embedded expectations that require too good an execution, I move on.

It is a superpower.

I was recently listening to an interview with Andrew Hollingworth, the founder of Holland Advisors. He operates with a very similar philosophy, specifically regarding his heavy bias toward owner–operators.

For him, and for me as well, seeing a founder with significant skin in the game is a signal that cuts through the static. It is a filter that allows you to ignore 90% of the public markets immediately. Of course, the risk is that you will brush off a valid idea too quickly. You will watch stocks you passed on at $10 run to $50. That is the price of admission.

However, a strong filter protects your downside and ensures that when you finally do say yes, you are standing on a foundation of high conviction. Your job is to pass. You must try to break the thesis.

Why I Am Selfishly Writing This Post

This leads me to a new mental model I have been grappling with this week. I have my usual list of high–quality signals – e.g. (strong) secular tailwinds, above-average industries, founder–led businesses, share cannibals that aggressively retire their own stock, and “spawners” that have the DNA to create new business lines from scratch.

But while reading a post by a fellow author who goes by the name Unemployed Value Degen, I stumbled upon a concept I had never formally integrated into my framework: the “toehold.”

He is one of the very few writers covering Tiger Brokers, a company I have discussed frequently on this blog and one that I currently own in a significant position in my portfolio. I had to read his take.

I found his analysis of the core thesis excellent, but what really stuck in my mind was his mention of IBKR’s small ownership stake (which I was of course aware of), but the idea that the ownership might be a (strong) predictive signal for superior future performance.

It was a “lightbulb” moment. In the spirit of Mohnish Pabrai, whenever I encounter a new mental model that carries genuine weight, I want to punch it into my brain. I want to do everything humanly possible to ensure it sticks. Thus, this post is, quite frankly, born of a very selfish motivation. I am writing this to force myself to memorize the concept, to test the logic, and to ensure that the next time I see a toehold in a filing, I recognize it as the potentially powerful signal it might be.

The concept of a toehold is deceptively simple, yet it carries implications that most retail investors completely overlook. If we can identify why a larger, more sophisticated competitor is willing to take a minority stake in a smaller peer, we gain access to a level of due diligence that no individual investor could ever hope to replicate. It is a form of validation that goes beyond what any spreadsheet can tell us.

Over the following segments, I want to explore why this happens, look at the historical evidence of outperformance, and understand how we can use this to sharpen our filters even further.

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More Than a Minority Interest: The Anatomy of a Toehold

To understand why this concept carries such weight, we first need to define what we are actually looking at. In the blog post that sparked my curiosity, Unemployed Value Degen laid out the case for UP Fintech (TIGR) by highlighting a specific shareholder:

“UP Fintech holdings is still controlled by its founder, Tianhua Wu, with over 12% of economic ownership, and voting control with class B shares that have 20x voting rights. There are also strategic ownership stakes held by Xiaomi, the mobile phone / automobile manufacturer, and Interactive Brokers. When looking at the Interactive Brokers’ ownership stake in particular, it qualifies as a “toehold” – a small ownership stake by a larger competitor. This is usually accompanied by technology and expertise transfers, indicating a bright future for the smaller company, and sometimes foreshadows an eventual acquisition. Sure enough, Interactive Brokers assisted TIGR with the US compliance and Know Your Client rules. The last time I bought a stock with a toehold, it was VitaCoco at $9, and today it trades at $65. Toeholds were a primary area of research of my former PhD advisor, and whenever I stumble across one, it immediately catches my attention.”

This passage touches on the idea of asymmetrical information. When a large, established player like Interactive Brokers takes a stake in a younger, more nimble competitor, they are frequently doing more than just parking capital. They are performing a level of due diligence that you and I, as outside investors, simply cannot access. They are looking under the hood, so to speak. When they write that check, they are effectively telling the market that this smaller company has passed the most rigorous audit possible – one conducted by its own rivals.

Why Competitors Make the Best Auditors

Most investors spend their time tracking institutional ownership – looking for which superstar investor is buying which stock – everyone gets excited during 13-F season!

And while that can be a useful signal, it is fundamentally different from a toehold. A professional (fundamental-oriented) fund manager is an expert in realms such as business and competitive advantage analysis, portfolio management, valuation, or capital allocation, but a competitor is an expert in the actual field the business operates in. If a dominant player in an industry decides to own a piece of a smaller challenger rather than trying to crush them out of existence, we should pay attention. It suggests that the smaller company possesses something the larger one lacks: perhaps a more efficient tech stack, a better user acquisition strategy, or a specific geographic advantage.

This is a kind of “validation signal” in its purest form. A toehold suggests that the incumbent sees the smaller company as a peer or a future partner rather than a nuisance.

In the case of Tiger Brokers, having Interactive Brokers – the gold standard for discount brokerage infrastructure – assist with US compliance is not just a “nice to have” partnership. It is a massive operational lift. It removes the execution risk that plagues so many high–growth fintechs.

“We currently rely on Interactive Brokers to execute, settle and clear a portion of the trades of the U.S. and Hong Kong stocks and other financial instruments, and to comply with certain federal, state and other laws, as discussed more fully in Item 4.B “Business Overview-Our Core Products and Services-Brokerage Services. ” For the years ended December 31, 2023, 2024 and 2025, 16.6%, 10.6% and 6.6% of our total net revenues were executed and cleared by Interactive Brokers. For consolidated accounts, the information of which is not fully disclosed to Interactive Brokers, we receive commission fees and direct a pre-determined portion to Interactive Brokers.“ - Tiger’s 2025 Annual Report

For us, the filter is simple. If one of the best brokerage businesses, one of the best brokerage infrastructure providers in the world, is betting on their regional competitor’s success, the probability of that competitor being a “fraud” or a “failure” drops meaningfully.

The Call Option on a Future Acquisition

Beyond the operational validation, a toehold represents a very specific type of financial optionality. It is sometimes (or rather often?) the first step in a “creeping takeover” attempt or a pre–negotiated path to an acquisition. When a larger company takes a 5% or 10% stake, they are often buying a seat at the table. They get a front–row view of the financial performance. Possibly a board seat. They build relationships with the management team. Most importantly, they often secure “right of first refusal” or “right of first offer” clauses that give them a massive advantage if the company ever decides to sell.

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As an investor, owning a company with a strategic toehold means you are essentially “piggybacking” on a future M&A event. You are holding a stock that has a built–in floor price because there is a logical, deep–pocketed buyer already sitting on the cap table.

This can create an interesting risk–reward profile. You get the upside of the smaller company’s organic growth, supplemented by the expertise of the larger partner, all while holding a “call option” on an eventual buyout at a premium. It is a powerful combination. It turns a standard equity investment into a strategic play.

Conceptualizing the toehold also requires us to look at it through the lens of signaling. In a world of “pump and dump” schemes and management teams that over-promise and under-deliver, the toehold is a concrete action. It is a “hard” signal. It costs the larger company real money and reputation. They are putting their name next to the smaller company. This isn’t a vague press release about a “strategic partnership” that leads nowhere. This is a real monetary commitment.

A toehold is a vote of confidence in the management’s integrity and the product’s longevity. It serves as a filter that filters out the “wannabes.” It is very rare for a world–class organization to take a minority stake in a mediocre business. They want the best. They want the winners. By identifying these stakes early, we are essentially letting the most successful companies in the world do our screening for us. We are leveraging their vast resources, their industry knowledge, and their strategic foresight to improve our own hit rate.

This is how we move from being “dumb money”, from “retail”, to being “informed.”

Case Studies

If a mental model is to be useful, it must move beyond theory and prove its worth in the cold reality of the tape.

One of the most profound examples in recent history, and one brought up by Unemployed Value Degen in the excerpt below, is the relationship between Keurig Dr Pepper and Vita Coco.

When a massive distributor and manufacturer takes a strategic stake in a category leader, they are not only betting on a trendy product; they are betting on the supply chain, the brand’s resonance with a specific demographic, and the potential to scale that brand through their own superior distribution network.

For the smaller company, it provides an immediate “inflection point” where their operational ceiling is effectively removed. As the stock moved from under $10 to over $60, it became clear that the toehold might have been the early signal for a massive compounding story.

The Constellation Software Playbook: Public Equities as a Lab

When we talk about capital allocation, the conversation among value investors will most likely eventually shift to Mark Leonard (I hope he recovers quickly!) and Constellation Software (CSU) and the accomplishments of Leonard and his team.

I believe a fascinating shift has occurred recently with both Constellation and its spin–off, Topicus. Traditionally, these companies were focused on acquiring 100% of small, “boring” vertical market software (VMS) companies. But lately, they have begun to flex their muscles in the public markets, taking strategic stakes in larger VMS peers.

I see this as a natural evolution of their acquisition approach.

By taking a minority interest in a public competitor, CSU and Topicus are essentially running a real–time laboratory. They get to observe the management’s discipline, the stickiness of the software, and the pricing power of the business from a privileged vantage point. At the same time, they can share their expertise with them. If the company performs well and fits the culture, the toehold might become the foundation for a full acquisition. If it doesn’t, they have still invested in a liquid high–quality asset that they understand better than almost anyone else in the market. It is a “try before you buy” strategy at a multi–million/billion dollar scale.

The Tencent Ecosystem: Minority Stakes as Call Options

Tencent has arguably perfected the “toehold” strategy more than any other firm in history. Instead of trying to build every service themselves, they took a minority stake in companies like Sea Limited, JD.com, and Meituan. They then integrated these companies into the WeChat ecosystem, providing them with a massive firehose of traffic. This didn’t just help the smaller companies grow – it allowed Tencent to build a “synthetic” monopoly where they owned a piece of everything (good) their users touched without the regulatory headache of owning the entire entity.

Tencent eventually divested some of these stakes or, in other cases, deepened them. But the signal was always there: if Tencent is taking a 5% or 10% stake in your business, they are essentially providing you with their “Seal of Approval.” They are saying that your tech, or your product offering, is very good.

More Examples

To give you a sense of just how widespread this phenomenon is, I’ve put together a few more examples where a strategic toehold was the precursor to a massive run or an eventual takeout. Notice how in each case, the “parent” company brought more than just cash to the table – they brought expertise, distribution, or technology.

• Coca–Cola & Monster Beverage: In 2014, Coke took a roughly 17% stake in Monster. They swapped their energy brands for Monster’s non–energy brands and took over global distribution. The stock has been a multi–bagger since, fueled by the “Coke System” acting as a global accelerant.

  

• Disney & BAMTech: Disney initially took a 33% stake in BAMTech – which was never a public company – to power its streaming aspirations. That toehold provided the technical foundation for Disney+, and Disney eventually bought the whole company. The minority stake was the “due diligence” period for their entire future strategy.

• Microsoft & Facebook (2007): Long before Facebook was the giant it is today, Microsoft took a 1.6% stake for $240 million. While small, it was a massive signal to the market that the social graph was the next frontier of computing.

  

• Delivery Hero & Glovo: This started as a 15% minority interest. Delivery Hero used that position to coordinate strategy and eventually moved to take full control.

• Celsius (CELH) & PepsiCo: This is perhaps the most striking modern parallel to the Monster/Coke deal. In 2022, PepsiCo took a strategic minority stake in Celsius – roughly 8.5% – worth about $550 million. On the surface, it may seems as only a capital injection. In reality, it was a total transformation of the business as the deal came with a long–term distribution agreement that plugged Celsius into Pepsi’s massive, world–class logistics machine. For an energy drink company, the ability to get “eyes on the shelf” is the ultimate “moat.” The toehold was possibly a signal that Celsius is graduating from a niche fitness brand to a global contender.

• Amazon & the Warrant Strategy: Amazon has perfected a very aggressive form of the toehold. They often take minority stakes – or warrants that can be converted into stakes – in their most critical infrastructure partners. One notable example is Air Transport Services Group (ATSG).

  

  They have done similar moves with hydrogen fuel cell providers like Plug Power. Here we might also bring up Amazon’s grocery (ad)ventures: While the 100% acquisition of Whole Foods was a massive, headline–grabbing event, Amazon’s quieter moves in the grocery sector are even more indicative of the toehold strategy. I’ve noticed they frequently use warrants and strategic investments to align themselves with essential partners. For example, they secured the right to acquire a minority stake in SpartanNash, a major US grocery distributor and retailer, a move to integrate Amazon’s logistics with a massive, pre-existing food distribution network. They executed a similar strategy in India with Future Retail, where they took a minority position through a subsidiary to navigate complex regulatory environments. In both cases, the investment served as a "backdoor" into a difficult industry where local expertise and existing infrastructure are the primary moats. It allowed Amazon to "learn" the supermarket business from the inside before committing to a full-scale assault on the market.

• Berkshire Hathaway & the Japanese Sogo Shosha: In 2020, Warren Buffett announced that Berkshire had acquired 5% stakes in each of Japan’s five largest trading houses. It was a classic toehold. He signaled to the market that these were high–quality, undervalued “share cannibals.” Since then, he has slowly increased those stakes toward 9%, and the performance has been spectacular. It was a vote of confidence in an entire sector that the rest of the world had ignored.

  

• Volkswagen & XPeng: In the automotive world, the toehold is often a confession of a technological gap. VW recently took a 4.99% stake in the Chinese EV maker XPeng. This wasn’t because VW needed a financial investment; they needed XPeng’s software and E/E architecture for the Chinese market. For an investor, seeing one of the world’s largest legacy manufacturers pay for the “intelligence” of a smaller, younger peer is a massive signal of who actually owns the superior tech stack.

You could continue this list almost indefinitely. What strikes me about these examples is the asymmetry. As an investor, you don’t need to be right about the eventual acquisition to win. You just need to be right that the toehold signals a high–quality business with a long runway. If the acquisition happens, it’s a bonus – a “pop” in valuation that serves as the cherry on top; or sometimes, you might not even want the company to be acquired, as it hinders you from compounding with the company for decades to come.

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So the key benefit of the returns comes from the operational validation that the toehold provides in the first place. It narrows your universe of potential investments down to the ones that have already been vetted by the best in the business.

Putting It Into Practice

To wrap up this piece, we need to move even further from the conceptual theory to the practical side of things. The logical next step is to institutionalize this signal by adding it to your formal investment checklist.

Every time you open a new filing or begin a deep dive into a small–cap name, you now want to find yourself asking a pointed question:

I have looked at the cap table to identify whether a strategic peer or a dominant industry competitor holds a non–controlling interest or warrants that serve as a validation signal for the company’s underlying technology or operational model. When was the position established and was it increased/decreased over time?

You might frame this in a few different ways.

• Is there a sophisticated competitor holding a non–controlling interest that suggests a hidden technology transfer?

• Does the ownership structure reveal an “unspoken” partnership that hasn’t yet hit the income statement?

• Or, perhaps most provocatively: If I were the CEO of the industry leader, what do I see in this smaller challenger that I cannot build myself?

These questions serve as another “violent filter.” They force me to look for the “smart money” that isn’t just looking for a quick trade, but for a structural, long–term advantage, and that may actively contribute to the thesis playing out; it’s a silent vote of confidence before it becomes common knowledge.

In one’s investing routine, this fits perfectly into the “turning over a lot of rocks” philosophy famously championed by Peter Lynch. As he often said, the person who turns over the most rocks wins the game. Your process maybe should include a mandatory stop at the ownership tables for every single company you look at.

I’ve found that tools like Koyfin make this process incredibly frictionless. Instead of wasting ten minutes digging through a dense proxy statement to find the relevant tables, I can pull up the institutional and insider ownership instantly.

I use my common sense. I look for the anomalies. If I see a tier–one competitor or a world–class strategic partner holding a 4.9% stake, my interest is immediately piqued.

I want to know since when and why they are there.

It should become a simple habit, but one that has the potential to reveal massive outliers that the rest of the market is ignoring because they are too busy looking at the quarterly earnings beats.

Another highly effective way to generate new investment ideas is to reverse the process entirely. Instead of searching for the toehold in a vacuum, you may start by identifying companies with what Mohnish Pabrai calls "Spawner DNA" – the rare breed of organizations like Amazon, Tencent, or even Nvidia that are culturally programmed to plant strategic seeds.

Look directly at the publicly traded minority investments these giants have made.

If you share this post with like-minded investors who may find value in this piece, it helps to spread the word 🙏🏻

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On one hand, the latest results in Grab posted (Q1 of FY26) reveal a business that is firing on all cylinders, delivering a massive $120 million profit for the period.

“Q1 is off to a fantastic start for us across all the financial fundamentals of our business.“ - Q1 Earnings Call

On the other hand, a sudden political intervention from Jakarta and a bruising regional oil shock have created near- and medium-term challenges.

President Prabowo Subianto’s announcement of a commission cap on ride-hailing services – effective immediately and specifically targeting the “ojol” motorcycle sector – briefly wiped 7% to 8% off the market capitalization as the news broke. When you combine that with a fuel crisis that saw prices spike sharply across the region in March, the bearish narrative had plenty of ammunition.

Market volatility is nothing new for Grab shareholders. If you look at the historical chart, the story is one of persistent struggle against gravity. We are, in fact, currently hovering around a critical support line that has been tested repeatedly over the last twelve months.

The technical picture remains bleak for long-term holders. A negative five-year CAGR is a tough pill to swallow for anyone who bought into the initial listing hype.

However, price and value are frequently distinct entities. While the stock initially buckled under the weight of the Indonesian news and the fuel headwinds during last week’s trading sessions, today’s pre-market reaction – up roughly 2% – suggests a more nuanced interpretation might be beginning to take hold.

This feels like a defensive posture. It is the sound of investors weighing a localized regulatory headwind and a short-term margin squeeze against a group-level performance that, frankly, exceeded some investors’ expectations.

Before I peel back the layers of this quarterly report, I recommend revisiting my broader thesis. You can find the foundational details in my Grab Holdings Deep Dive Series (four parts), where I explore the business model, the ecosystem’s structural moats, and the long-term unit economics of the superapp model, the management team, and the stock’s valuation.

What I will discuss in this analysis is that, personally, I view the “Indonesia shock” and the fuel crisis not as terminal blows, but as “Darwinian filters.” The panic selling we saw earlier this week stems from Indonesia’s significant footprint. It accounts for roughly 21% of group revenue, with mobility specifically representing 36% of that total. A mandated drop in platform commissions from 20% to a mere 8% for motorcycle rides is, on the surface, a brutal hit to the top line. But as I’ll explore in the coming segments, the mechanical reality is far more complex.

I am increasingly convinced that we are seeing a “scale economies shared” business in the making. Much like the regulatory shifts that have historically impacted companies like Edenred, these interventions – alongside the exogenous pressure of rising fuel costs – often end up entrenching the dominant player. When margins are squeezed by decree or by the pump, only the most efficient, best-capitalized, and technologically superior operators survive.

The question is whether the market can look past the immediate revenue “shock” and see the reinforced moat being built underneath. I think we are seeing a fundamental decoupling of operating results from share price sentiment.

I find myself surprisingly relaxed. The runway is long. The flywheel is accelerating. And as we transition into the granular details of the Q1 results, I want you to keep one thought in mind: in a world of 8% commission caps and expensive fuel, the only thing that matters is who has the lowest cost of operation. Right now, that is Grab.

Disclaimer: I own Grab shares. 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.

Q1 2026 Highlights: Seventeen Quarters of Momentum

Usually, the first quarter in Southeast Asia is a period of “holiday hangovers” where the festivities of Lunar New Year and Ramadan dampen both delivery demand and driver availability.

“… the confluence of Lunar New Year and Ramadan within the first quarter, both in the first quarter this year, creating acute supply pressures as usual during those 2 festive periods.“ - Earnings Call

Yet, the latest Grab results tell a different story.

In Q1, Grab reached an all-time revenue high of $955 million, a 24% leap year-on-year (19% YoY on a constant currency basis) that suggests the platform is still finding new pockets of demand.

Group MTUs increased both YoY (16%) and QoQ to 52 million.

In addition, we are looking at the seventeenth consecutive quarter of Adjusted EBITDA growth. This is a testament to a compounding machine that has finally learned how to generate profits.

“As we look ahead to the rest of the year, we remain committed to delivering durable, profitable growth while standing shoulder-to-shoulder with our communities — leaning deeply into AI to outserve our users with hyper-personalized experiences, while simultaneously unlocking more sustainable earnings opportunities for our ecosystem partners.“ - Anthony Tan

Grab posted $120 million in profit for the period. The business is now self-sustaining. It generated $98 million in Adjusted Free Cash Flow this quarter alone, pushing its trailing twelve–month total to a very healthy $489 million (remember that management aims for $1.2 billion in FCF by 2028, so we’re very well on track).

For those of us who remember the days of billion–dollar annual burn rates, this feels like a historical pivot point.

This profitability is not a result of “starving” the ecosystem. On the contrary, user engagement is at record levels. As mentioned, Monthly Transacting Users (MTUs) grew 16% to 51.6 million, and the On-Demand GMV accelerated to $6.1 billion.

I find the segment–level efficiency particularly revealing. In the Deliveries division, GMV reached $3.9 billion despite the festive season. This was driven by a clever “barbell” strategy. On one end, affordability initiatives like Saver Deliveries and Group Orders brought in the volume. On the other hand, the segment’s Adjusted EBITDA margin improved to 2.3% of GMV (from 2%). It seems like they are capturing the budget–conscious user without sacrificing the overall margin profile.

Mobility followed a similar path, with GMV reaching $2.2 billion. Interestingly, transactions grew faster than GMV at 28%, indicating that the service is becoming more accessible and high–frequency rather than just more expensive.

They are maintaining a steady 8.9% Adjusted EBITDA margin here, even while navigating the sharp regional fuel crisis that could have easily derailed a less efficient operator.

On the autonomous vehicle side of things, Grab is clocking 40,000 autonomous kilometers in a controlled Singapore residential estate, learning how to integrate hybrid fleets of human and AI drivers. They want to be the most experienced operator in Southeast Asia, ready to plug in any AV software player when the technology and the regulators are finally ready. However, as noted on the earnings call,

“… the adoption of AVs in Southeast Asia remains nascent. We see governments and regulators taking a measured approach in implementing AVs, which we believe is the right approach for our region. We will continue to incorporate AVs in our platform at a pace that reflects the trust communities place in us and our emphasis on customer safety. To be clear, we do not expect anyone to be able to deploy impactful disruption to our human driver network in the near future.“

If there was a “shock” this quarter beyond the Indonesian regulation, it was the explosive growth of Financial Services. The $1 billion milestone in quarterly loan disbursals is hard to ignore.

That is a 67% year-on-year increase.

More importantly, the Gross Loan Portfolio has swelled to $1.4 billion – even though QoQ growth was more modest – while credit discipline and credit quality actually improved. They are lending into their own ecosystem – to drivers and merchants, and consumers (app users), all of which they already know – which creates a massive information advantage for their underwriting AI.

According to Grab’s management team, “more than 1/3 of that incremental revenue dropped straight to the bottom line for Financial Services, demonstrating that operating leverage that we've been speaking about.“

So this segment is on a clear trajectory to break even in the second half of 2026.

“We also reiterate our expectations to exit 2026 with a Gross Loan Portfolio of over $2 billion and achieve Segment Adjusted EBITDA breakeven in the second half of 2026 for the overall Financial Services segment.“ - Earnings Call

It is becoming a legitimate third pillar for the group, not just a loss–leading side project.

Furthermore, management is increasingly talking about the “Grab Intelligence Layer” as their ultimate competitive moat – 20 billion transactions of historical data built over 14 years are hard to replicate.

“We capture highly localized real-time data on how over 50 million users and partners interact across 8 markets […] We feed these multimodal signals, from hyperlocal mapping to in-store payment terminals, into our AI Intelligence Layer to optimize our marketplace efficiency, from dynamic pricing to last-mile routing. Crucially, we paired this data advantage with our massive through physical fulfillment network. That closed loop system or ecosystem is our biggest competitive moat, which is why our AI investments translate directly into measurable financial outcomes.“ - Earnings Call

For instance, consider the “Turbo” mode for drivers. Those who opted into this AI–powered optimization saw a 23% uplift in earnings per hour. It is a simple equation: more efficient routing means more jobs per hour, which protects driver livelihoods even when fuel prices are at record highs (here Grab tried to cushion the impact by deploying targeted fuel rebates and proactively engaging with regulators across their markets).

“This has contributed to Mobility transactions growth outpacing Mobility GMV growth, with transactions up 28% YoY.“

On the fuel crisis subject, to provide some more context, management also said the following on the call:

“In the medium term, we are committed to accelerate the EV transition to reduce our driver partners exposure to fuel price volatility. So for example, in Thailand and Philippines, we have a drive-to-own program that connects our drivers with OEMs like BYD and GAC, where we have deals of up to 70,000 vehicles available across 6 markets with accessing to financing so they can own those more easily.

In Vietnam, we have secured preferential charging rates also through our charging network partners, EBOOST and Charge+, which helps our drivers in the transition also. And finally, in Thailand, I am pleased to say that our total fleet supply has crossed 30,000 EVs on the platform and demand for those from consumers is also strong, where they can select that EV option, and that demand has grown by over 35% year-on-year. So this fuel crisis has become an opportunity in the sense that it helps us to accelerate that EV transition.“

For merchants, the AI Assistant is driving a 15% GMV uplift for those who engage with it.

The high-margin ad business is also developing favorably: average advertiser spend grew 44% YoY as merchants see increasing measurable returns. However, as @the_zack_zhu shared on X (see overview below), the number of active advertisers only grew 5%, and it remains to be seen whether Grab has already hit a structural ceiling or whether this is just a temporary slowdown (I’m leaning towards the latter).

Overall, Grab is “winning” because they are out-serving the competition by making the platform more profitable for the people who actually do the work.

The strategic horizon is also expanding. The acquisition of foodpanda’s business in Taiwan is a bold move into a high–ARPU market outside their traditional Southeast Asian stronghold. And importantly, the Q1 figures and the outlook do not include revenue from both the Stash and Foodpanda Taiwan acquisitions.

The outlook remains remarkably steady despite the near-term noise. Grab reiterated its full–year 2026 guidance, targeting revenue up to $4.10 billion and Adjusted EBITDA as high as $720 million.

As readers of my initial deep dive know, they are also putting their money where their mouth is with a $500 million share repurchase agreement.

“In March 2026, we entered into an accelerated share repurchase agreement and a contingent forward purchase agreement to repurchase $250 million and up to $150 million, respectively, worth of Class A ordinary shares, as part of our previously announced $500 million share repurchase programme approved by the Board of Directors in February 2026.“

At the same time, it’s important to note that nonetheless, the basic and diluted shares have increased both year-over-year and quarter-on-quarter.

However, management added some more nuance on this subject during the call:

“So a total of $400 million has been accelerated, which means only in the market for 5 to 5 trading days in Q1 itself. So you can’t look at it in isolation. Now both these programs are expected to be executed over the next 4 months. So I’ll share a lot more in the next quarterly earnings when we look at the Q2 results. Now in terms of share count, it would have amount to roughly around 2% of our total share count, which will more than offset for the dilution from stock-based compensation. So that’s how we’re viewing it. As a reminder, there’s still another $100 million left to go in the share buyback program, and we’ll continue to have discussions around capital allocations with our Board.“

The Indonesia Shock: Regulatory Caps and Its Implications – Is the Grab Thesis Now Broken?

Indonesia, one of Grab’s largest and most important markets, just threw a curveball at investors. In early May, the government introduced price caps on ride-hailing fares through Presidential Decree No. 27/2026, aiming to protect drivers and curb rising ride costs for consumers.

On the earnings call, management clarified that the regulations will specifically target the “ojol” (short for ojek online) motorcycle sector.

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