On Wednesday I walked you through the $100 billion sovereign AI buildout that almost nobody in financial media is paying attention to. I explained the CLOUD Act, why foreign governments cannot legally build their most sensitive AI systems on American cloud infrastructure, and why countries from Saudi Arabia to France to the UAE are now writing generational checks to construct their own. I ended that piece by telling you I would show you the company I think is best positioned to capture that spending, and today I am keeping that promise.

What I found when I went digging is an $84 million market cap company that controls 358 megawatts of powered land across three sovereign markets, has real revenue and real EBITDA running through the door as of three weeks ago, is fielding inbound interest from hyperscalers on its flagship facility, and has a spin-off on deck at a valuation more than ten times the entire parent company’s current market cap. The Wall Street analysts have not written it up yet and the institutional money is not in it yet, which is exactly why I am writing about it today.

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Dear Friend,

I hope you have had a good week so far!

In 2018, Congress quietly passed a law called the CLOUD Act that gave the U.S. government legal authority to demand data from American cloud providers regardless of where in the world that data is physically stored. Most Americans never heard of it. But outside the United States, it triggered a geopolitical chain reaction that is now reshaping the entire AI infrastructure landscape in ways almost no investor is paying attention to.

Every investor reading this newsletter already knows the American AI buildout is real. I have written about it extensively. The hyperscalers are spending $805 billion this year and over a trillion next year building data centers, locking down power, and deploying GPUs at a pace the world has never seen. That spending trajectory is not slowing down.

But here is what almost nobody is talking about yet. That buildout you have been watching, the one happening across Texas and Virginia and the American West, is only half the story. There is an entirely separate infrastructure buildout happening simultaneously across Europe, the Middle East, and Asia that is being driven by a completely different set of buyers with a completely different motivation. Foreign governments looked at the CLOUD Act and arrived at the same conclusion: they cannot build the most important technology of their generation on American infrastructure, under American laws. So they are building their own.

I am talking about sovereign nations with balance sheets larger than most publicly traded companies on earth, spending tens of billions of dollars each to construct AI infrastructure on their own soil, under their own laws, answerable to their own governments. The total committed capital I have tracked across these programs is staggering, and I am going to walk you through it country by country later in this piece.

Most investors have never heard of any of this. They are still debating which chatbot company wins the model race while an entire parallel infrastructure economy is being constructed across four continents. That is the opportunity I am writing about today.

The sovereign AI buildout is not a separate trade from the AI infrastructure thesis you already follow. It is a demand multiplier for the exact same stack: compute, power, cooling, physical data centers, all needing to be built inside specific national borders by companies that can operate outside American legal jurisdiction. The companies positioned at the intersection of that demand and that constraint are, in my view, some of the most asymmetrically undervalued names in the entire AI trade right now.

The Law That Changed Everything

If you have been following the AI infrastructure trade, you already know the American buildout is one of the biggest capital deployment cycles in modern history. What I want to talk about today is the second buildout. The one almost nobody is paying attention to.

Right now, governments across Europe, the Middle East, and Asia are racing to construct their own AI infrastructure from the ground up. Their own data centers, their own compute clusters, their own power solutions, all built on domestic soil and operated under domestic law. This is not a future trend. The commitments have been made and the capital is allocated, but the actual construction and deployment cycle is still in its early innings. If I had to place it on the American timeline, I would say the sovereign AI buildout today is roughly where the U.S. hyperscaler cycle was in 2023. The spending has started, but the acceleration is still ahead of us.

To understand why this is happening, and why it is accelerating so fast, you need to understand one American law that most investors have never heard of.

The CLOUD Act, passed in March 2018, does something very simple with very large consequences. It gives U.S. law enforcement the legal authority to compel any American technology company to hand over data it controls, regardless of where in the world that data is physically stored. The jurisdiction does not follow the server. It follows the company. If the company is American, the data is reachable. A server sitting in a data center in Frankfurt operated by Amazon Web Services is, under this law, just as accessible to a U.S. warrant as a server sitting in Virginia.

For most Americans that sounds like a reasonable law enforcement tool. For the rest of the world it created an immediate and structural problem.

The European Union already had its own data protection framework in place. The GDPR, which went into effect just two months after the CLOUD Act was signed, explicitly prohibits the transfer of personal data to foreign governments unless that transfer is authorized by an international agreement.

Article 48 of the GDPR is clear: a foreign court order alone is not a lawful basis for transferring EU data. The CLOUD Act does not care. It requires American companies to comply with valid U.S. government demands even when doing so directly violates the laws of the country where the data sits. Those two legal frameworks are not in tension. They are in direct contradiction, and no contract, data processing agreement, or marketing language about data residency resolves the underlying conflict.

Last summer that contradiction was confirmed under oath in the most direct terms possible. Anton Carniaux, Microsoft France’s Director of Public and Legal Affairs, testified before the French Senate during a formal inquiry into digital sovereignty. A senator asked him point blank whether he could guarantee that French citizens’ data held through government procurement contracts would never be transmitted to U.S. authorities without explicit French authorization. His answer, under oath: no, he could not guarantee it. Not because Microsoft is acting in bad faith. Because it is an American company, and the law does not give it a choice.

Now layer the AI buildout on top of this.

Every country on earth is watching the same thing we are watching. AI is becoming the infrastructure layer underneath everything. Healthcare, defense, financial systems, energy grids, government services. The nations building this technology into their critical systems are making a decision that will last decades. And the CLOUD Act forced a very specific version of that decision: do we build our most sensitive AI capabilities on American infrastructure that is legally reachable by a foreign government, or do we build our own?

One by one, they are choosing to build their own. And the checks they are writing are enormous.

Here is the part that matters for your portfolio. This is no longer a legal debate or a policy discussion happening in Brussels. It is a procurement decision being made by governments with sovereign wealth funds, state-owned energy companies, and national security budgets behind it.

The question stopped being whether foreign nations are uncomfortable with American cloud infrastructure. That was settled years ago.

The question now is how much capital is about to flow into building the replacement, which companies are positioned to capture that spending, and whether the market has priced any of it in yet. From everything I have seen, it has not even come close.

The $100 Billion Sovereign Buildout

I want to give you a sense of the scale here because the numbers are genuinely staggering. If you are wondering why any of this matters for your portfolio, think about it this way. The publicly traded companies that are positioning themselves at the forefront of this buildout right now, before the market catches on, are the ones that stand to win in a massive way.

The World Economic Forum and Bain published a report at Davos in January 2026 that put a number on something I had been tracking for a while. The United States and China together capture roughly 65% of all investment across the entire AI value chain. Chips, cloud, data centers, models, everything. Two countries control nearly two thirds of the capital flowing into the most important technology on earth. That concentration is exactly why every other major nation is now scrambling to build its own stack.

The numbers:

Saudi Arabia launched Project Transcendence, a $100 billion program backed by the Public Investment Fund and chaired by the Crown Prince. This is not a venture fund. This is a nation building an entire AI ecosystem from scratch with the same long-term commitment they brought to their energy infrastructure.

Japan approved its first national AI plan in December 2025 and committed over $65 billion in public support for AI and semiconductor infrastructure through 2030. The government openly admitted it had fallen behind and is treating this as a catch-up sprint.

France took a different approach entirely. They do not have the biggest budget, but they have something almost nobody else has: a massive surplus of clean nuclear power. President Macron anchored a $109 billion AI investment package around that energy advantage, positioning France as the place where sovereign AI gets built because the power economics are unbeatable.

India hosted the AI Impact Summit in February 2026 and triggered over $200 billion in committed investment, including a deal with the UAE to deploy an 8-exaflop supercomputer on Indian soil.

And the UAE itself is building a 5-gigawatt AI campus in Abu Dhabi estimated at over $30 billion while simultaneously exporting sovereign AI frameworks to nations across Asia and Africa through its G42 platform.

These are not tech companies raising Series B rounds. These are nations with sovereign wealth funds and state-owned energy companies writing generational checks.

Morgan Stanley’s top recommended strategy for 2026 says it plainly: focus on the beneficiaries as nations pursue self-sufficiency in energy, critical materials, manufacturing capacity, and AI capabilities.

Tomorrow I am publishing a deep dive for paid subscribers on my single most convicted name in the entire sovereign AI trade. A company that has already secured a foothold in this buildout through a deal most investors have completely overlooked. I will break down the thesis, the catalysts, and the price levels I am watching.

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The first Cold War created generational wealth for investors who understood the infrastructure trade before consensus arrived. Defense contractors, semiconductor companies, telecommunications networks, satellite systems. The money was not made by the people debating ideology. It was made by the people who recognized that both sides were going to spend whatever it took to build the physical systems underneath their ambitions, and who invested in the companies supplying that buildout before Wall Street caught on.

The AI Cold War has the same structure. The same timeline. And the same asymmetric opportunity.

Most investors are still focused on the model layer. Which chatbot wins. Which AI company raises the next mega-round. That is the headline story, and it will stay the headline story for a while. But the real money in this cycle, just like every infrastructure cycle before it, is being made underneath the headlines. In the power, the land, the data centers, and the compute capacity that every sovereign nation on earth now needs to build for itself.

The companies positioned at the intersection of that demand are not household names yet. Some of them are still being priced by the market as something they used to be rather than what they are becoming. That is where the asymmetry lives. If this piece made you think differently about where the AI infrastructure trade is heading, tomorrow’s deep dive is where I put the money behind it.

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Cheers,

Matt Allen

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Dear Friend,

I hope you are having a great start to the week! In my opinion piece for the week, I am diving into my favorite investment theme.

During the gold rush, thousands of miners flooded California chasing their fortune. Most went home broke. The ones who actually got rich were the merchants selling picks and shovels, the railroad owners moving people and supplies, and the landowners who happened to own the ground the tracks ran through. It didn’t matter whether any individual miner struck gold. The infrastructure around the rush got paid regardless.

That’s the lens I’ve been using to look at AI for the past few years. Everyone is chasing the model companies, the software plays, the next big name. I’ve been focused on something quieter. The ground underneath all of it.

Last week Goldman Sachs and Morgan Stanley both published research on the AI infrastructure buildout. Different angles, same conclusion. The numbers are bigger than they appear, they’re still accelerating, and we are earlier in this cycle than most people realize.

If you want to understand where the real money flows in the AI economy, and which companies are quietly becoming the infrastructure owners of this era, this is the most important thing I have written all year.

The Numbers

Let me start with what Morgan Stanley is seeing.

Their thesis is straightforward. The biggest technology companies in the world are in a race they cannot afford to lose. Falling behind on AI infrastructure does not just mean missing an opportunity. It means becoming irrelevant in the most important technology shift of our lifetime. So they are spending like it.

Five companies. Amazon, Google, Meta, Microsoft, and Oracle. This year alone they are expected to spend $805 billion on AI infrastructure. Next year that number climbs to $1.1 trillion. Morgan Stanley just raised those estimates. The prior forecast was $765 billion for 2026 and $951 billion for 2027.

That single year 2026 figure is roughly equal to what every non-tech company in the S&P 500 spent on capital investment combined last year. Nearly double 2025 levels. Triple 2024. Meta just raised its full year guidance to between $125 and $145 billion. Alphabet spent $35 billion in the first quarter of 2026 alone. Amazon is approaching $200 billion for the full year.

And here is the thing about these estimates. At the start of both 2024 and 2025, Wall Street consensus had capex growing around 20% for the year. Both years it came in above 50%. The forecasters have been wrong in the same direction two years running. Morgan Stanley just raised their numbers again. The pattern is clear.

Goldman Sachs is asking a different question. Not how much are they spending, but what assumptions are baked into that number and how much bigger could it actually get.

Their answer is sobering.

Goldman models $7.6 trillion in cumulative AI infrastructure spend between 2026 and 2031 across compute, data centers, and power. But they are clear that this is not a forecast. It is a range built on assumptions that are more likely to move higher than lower.

Data centers that cost roughly $10 million per megawatt to build a few years ago now cost between $15 and $20 million (today’s AI chips run so hot and draw so much power that the buildings housing them have to be completely redesigned from the ground up, think industrial liquid cooling systems instead of fans, and entirely new power delivery infrastructure.)

Silicon is turning over faster than traditional accounting schedules reflect (the chips being bought today may be economically obsolete in three years even if the accounting says they last five, because each new generation of AI chips is so much more powerful that companies feel pressure to upgrade before the old ones are fully paid off.)

Construction bottlenecks in power and specialized labor are real and are not resolving quickly (building a new AI data center requires connecting to the power grid, and in many parts of the country the queue to get that connection approved is years long, which means capital is committed but capacity is delayed.)

Every one of those variables points in the same direction. Up.

Goldman even buried a conclusion on the last page of their report that most people skipped right over.

In their own words: “The success of the build-out for today’s AI ambitions may be what ensures that it is not enough for tomorrow’s technological opportunities.”

In other words, the infrastructure being built right now will create entirely new waves of demand that do not yet exist. The cycle feeds itself.

Here is the thing. Morgan Stanley and Goldman are looking at this from completely different angles. One is tracking the demand. One is stress testing the supply. They don’t agree on every assumption.

But for us as investors, we don’t really need them to. What matters is that two of the most respected research desks on Wall Street are both telling you the same thing. Enormous amounts of capital are flowing into AI infrastructure, the number keeps going up, and the end is nowhere in sight. They both accidentally proved my thesis at the same time.

Let’s get into it.

Why the Spending Never Stops

Now let me explain why the spending does not stop.

Every AI system does two things. First it trains. ChatGPT read and processed nearly the entire internet, billions of books, research papers, and databases to learn everything it knows. That process required months of computing time across tens of thousands of specialized chips running simultaneously. That is training. It is expensive, power intensive, and the models need to do it repeatedly as they improve and expand their capabilities.

Then there is inference. Every single time you type a question into ChatGPT and it responds, that is inference. Every time your email auto-completes a sentence, every time your bank flags a suspicious transaction, every time a business runs an AI agent to handle customer service or analyze data, that is inference happening in real time.

The huge opportunity here is when ai agents are unleashed at scale.

Right now we are still in the very early innings of businesses actually deploying AI into their daily operations. The small accounting firm using an AI agent to process invoices. The hospital using AI to read scans. The law firm using AI to review contracts. The retailer using AI to manage inventory. Every single one of those deployments adds inference demand to the system permanently. It does not turn off. It does not slow down. It compounds. As that adoption spreads across millions of businesses globally, the computing infrastructure required to support it grows with it.

We are not close to the ceiling on that demand. We are barely off the floor.

That is what $805 billion this year and $1.1 trillion next year is trying to keep up with. And when you understand that, one question becomes unavoidable. Who actually owns the infrastructure that all of this runs on?

The Neoclouds

All of that capital has to land somewhere. And this is where it gets interesting.

Most people assume the hyperscalers are building everything themselves. Some of it yes. But here is what most retail investors do not know. Microsoft, Meta, and others are actively signing multi-billion dollar contracts with a category of companies called neoclouds because they cannot build capacity fast enough on their own. Even the biggest technology companies on earth are renting from the landlords.

A neocloud is a company that builds and operates AI-optimized data centers and rents that computing power to whoever needs it. But not all neoclouds are built the same and that distinction matters for how you invest in them.

Some own just the building. They construct the data center, manage the power and cooling, and lease the physical space on long term contracts. Applied Digital, which I sent to subscribers at $9, operates here. They build the warehouse and hand the keys to companies like CoreWeave who fill it with GPUs and run the operation.

Some own the building and the GPUs inside it, renting raw computing power by the hour to companies that need it without signing a hyperscaler enterprise agreement. This is where most of the action in the category lives.

Some go even further and build a proprietary software layer on top of the hardware. That software makes their platform easier to use, more efficient, and more valuable, which lets them charge significantly more per hour and attract larger enterprise customers. Nebius, which I sent to subscribers at $28, operates here. Meta just committed up to $27 billion to them. CoreWeave sits in this tier too, with $66.8 billion in contracted revenue backlog already locked in.

The entire category is being validated in real time by the same hyperscalers everyone assumes are the competition. That is the part of this story that most people are completely missing.

Now the two names I am most focused on right now are MARA and IREN. This is a free letter so I am not going deep on either. That lives behind the paywall. But here is what you need to understand about both.

MARA is the most misunderstood stock in AI right now. Most people still see a Bitcoin miner. What they are actually building is one of the most strategically intelligent energy and compute infrastructure platforms in the entire space.

The $1.5 billion Long Ridge acquisition is the clearest signal yet. It gives MARA control of a 505 megawatt power plant sitting on over 1,600 acres with power, land, water, fiber, fuel supply, and grid interconnection already in place. That last part is everything. Grid interconnection is the single hardest thing to acquire in AI infrastructure right now. The queues to get approved can run years long. MARA did not wait in line. They bought the front of it.

Then there is the Starwood Digital partnership. Starwood Capital Group is one of the most respected institutional real estate and infrastructure capital allocators in the world, with decades of experience developing and operating large scale physical assets. When a firm like Starwood puts their capital and their relationships behind your sites, it means the largest hyperscaler tenants on earth are paying attention. Together the partnership targets over 2.5 gigawatts of total capacity, which opens the door to the biggest cloud customers in the market.

And then there is the piece almost nobody is talking about. The Exaion acquisition.

When a German bank, a French defense contractor, or a Middle Eastern sovereign wealth fund stores data on AWS, Azure, or Google Cloud, U.S. law gives the federal government the ability to request access to that data regardless of where the servers physically sit. That is the U.S. Cloud Act. For most international institutions handling sensitive data, that is a complete non-starter. They need infrastructure that operates entirely outside U.S. legal jurisdiction.

Exaion solves that. It is a subsidiary of EDF, the French state-owned energy giant, and it gives MARA a sovereign cloud solution that international companies and governments can actually trust. MARA already has 240 megawatts live in the UAE. The sovereign cloud opportunity globally is one of the most underappreciated markets in AI infrastructure. And MARA is already in it without needing to dilute shareholders to fund the capex. The CEO said it simply. Electrons are the new oil. I believe him.

Iren is the real time proof that this entire thesis executes. What separates IREN from most companies in this space is that they own the entire stack themselves. The land, the grid-connected power, the data centers, the GPUs, and the software layer on top. Most competitors outsource pieces of that. IREN builds and operates all of it in house, which means faster delivery, tighter cost control, and a structural advantage in the one area where everyone else is struggling most: getting capacity online quickly.

The numbers reflect that. IREN has over 4.5 gigawatts of secured grid-connected power across Texas, Oklahoma, and British Columbia, $2.3 billion in contracted AI revenue already on the books, and a $9.7 billion agreement with Microsoft that is progressing on schedule across multiple sites. They secured $3.6 billion in GPU financing from Goldman Sachs and J.P. Morgan at below 6% interest, with Microsoft’s own prepayment covering 95% of GPU-related capex alongside that facility. This week they energized their Sweetwater 1 site in Texas, a 1.4 gigawatt facility now connected to the ERCOT grid, delivered on schedule. In a buildout where power delays are the norm and grid interconnection queues run years long, IREN just cleared the hardest hurdle in the business cleanly and on time. Their own earnings deck says it best: demand is not a constraint. The focus is on building the right long-term partnerships.

The companies that saw the AI wave coming and pivoted their existing power assets and infrastructure toward compute did not have to start from scratch. They already had the two things everyone else is desperately trying to acquire right now. Power and land. They just changed what they were computing.

Goldman mapped the infrastructure assumptions. Morgan Stanley mapped the spending trajectory. The math points the same direction from both ends.

While the rest of the market debates whether AI is overhyped, the five largest technology companies on earth are about to spend over $800 billion in a single year building the physical foundation of the next economy. Goldman told you the cost of that foundation keeps going up. Morgan Stanley told you the spending keeps accelerating. And the neoclouds are sitting at the intersection of both of those forces, owning the ground that all of it runs on.

Remember the gold rush. The miners got the headlines. The people who owned the infrastructure underneath got the wealth.

History does not repeat. But it rhymes loudly.

BeanFlagShip AutoPilot Investor Update

BeanFlagship is a concentrated growth portfolio built on two things I’ve tracked obsessively for years: emerging tech and geopolitical inflection points. You can invest directly alongside me, on autopilot, right here.

Invest With Me Now

I launched this fund on the same day growth stocks cratered, and a few weeks before the war broke out. (Great timing!) As a growth stock fund, we took a beating like everyone else in this space. I have learned a few lessons that will help us out in the future. Fortunately, we have finally clawed back.I am adding two plays to the portfolio. Both are built around AI infrastructure. Here is why.

DRAM (Roundhill Memory ETF): Memory is the bottleneck of the AI revolution and nobody is talking about it. Every AI model, every inference call, every data center workload runs through memory. Memory is what allows a chip to access and process data instantly rather than waiting to retrieve it from storage. The GPU gets all the attention but nothing moves without the memory behind it. Samsung and SK Hynix control the global memory market and this ETF is the most direct way to own both. I wanted the exposure without concentrating into a single name.

USA Rare Earth (USAR): The Trump administration invested $1.6 billion into USAR and took a direct equity stake. That alone tells you something. Then in April, USAR announced a $2.8 billion acquisition of Serra Verde Group, a Brazilian rare earth mining company that produces all four magnetic rare earths. Rare earths are the raw material behind every AI chip, every electric motor, every defense system being built right now. The U.S. has been dependent on China for this supply chain for decades. USAR is the domestic solution and the government is backing it with real money.

We sold Permian Resources for a 44% gain. Oil served its purpose in this portfolio but the upside from here is no longer compelling and volatility is picking up. We took the profit and redeployed it.

We also sold Ambarella for a 9% gain. The thesis was never broken but my conviction in memory as a category is stronger. DRAM is the cleaner expression of where I want to be in semiconductors right now.

See you on Wednesday!

Matt Allen

Dear Friend,

As I said last Wednesday, we are starting a new weekly newsletter on Monday’s that is more opinion based rather than educational based.

Why Humanoid Robots Could Be the Biggest Investment Opportunity of Our Lifetime

Every few decades, something comes along that completely rewrites the economy. The internet did it. So did the smartphone. I believe humanoid robots and embodied AI are next.

It’s easy to dismiss robots as science fiction. Something cool to watch on YouTube, but not worth taking seriously as an investor. That’s a mistake. The numbers, the companies, and the capital all point to the same thing: this is real, it’s happening fast, and it could be one of the biggest investment opportunities of our lifetime.

Let’s break it down.

First, a quick note on what this actually is.

We’re not just talking about machines. We’re talking about a new class of robots that can see, move, learn, and respond in the physical world. That’s what “embodied AI” means. It’s artificial intelligence not stuck in a screen, but physically operating in a warehouse, a hospital, or a home.

A humanoid robot is one example. These are robots designed to mimic human movement and capabilities, and they’re being trained using the same kind of foundation models that power ChatGPT. But they’re not alone. You also have autonomous delivery robots navigating city streets. You have surgical robots performing complex procedures. You have robotic arms on factory floors, working alongside humans in real time.

It’s all part of a movement I call Physical AI. And the growth projections are staggering.

Morgan Stanley sees a future with 1 billion humanoids generating $5 trillion in revenue every year by 2050. But the more immediate picture is equally compelling. Research from Fortune Business Insights and MarketsandMarkets shows the global humanoid robot market is projected to grow from $3.28 billion in 2024 to $66 billion by 2032, a 45.5% compound annual growth rate. The embodied AI market is expanding even faster, growing from $4.44 billion in 2025 to $23.06 billion by 2030. The broader autonomous robotics market is expected to reach $15.87 billion by 2032. These aren’t niche markets anymore. They’re becoming mainstream industrial infrastructure.

But this isn’t just about the market size. It’s about why this matters.

The labor shortage is one of the most urgent problems in the global economy right now. In 2025, 75% of employers say they can’t fill job openings. That’s up from just 38% a decade ago. In the U.S., there are 1.5 million more job openings than unemployed workers, and 4.5% of total labor demand is unfilled.

Nowhere is this more obvious than in manufacturing. There are 603,000 unfilled manufacturing jobs as of May 2024, projected to grow to 2.1 million by 2030. That could cost the economy $1 trillion annually. But the deeper problem is structural. Projections from Deloitte and The Manufacturing Institute show the manufacturing sector will need 3.8 million new employees by 2033, with 1.9 million positions (nearly 50%) likely to remain unfilled. Manufacturing has the highest economic multiplier of any industry, adding $2.74 for every $1 spent. That means this isn’t just a hiring problem, it’s a permanent production capacity gap that represents lost GDP and companies that can’t scale.

And it’s only getting worse.

Every day, 10,000 baby boomers turn 65. Data from the Census Bureau and Pew Research reveal the scope of this demographic shift. Between 2024 and 2030, roughly 30.4 million baby boomers will reach retirement age. That’s not just a workforce transition, it’s a demographic cliff. To keep up, companies need to hire 240,000 people per month to replace retirees. In the skilled trades, three workers are retiring for every one who enters the field. Construction alone will face a 245,000 worker loss by 2032. And unlike previous labor crises, this can’t be solved by immigration policy or education programs alone, because it’s happening across all developed economies simultaneously. Japan, Germany, and South Korea are facing even steeper demographic declines. The global workforce is shrinking, not expanding.

Healthcare is in crisis for the same reason. The U.S. faces a shortage of approximately 295,800 registered nurses today, with studies from McKinsey and the American Association of Colleges of Nursing projecting shortages of 200,000 to 450,000 by 2025 and up to 120,000 physicians by 2034. One in six U.S. hospitals have reported critical nursing shortages, and hospitals are closing entire departments due to staffing constraints.

Globally, the World Health Organization projects a 10 million worker shortage in healthcare by 2030. Unlike manufacturing, you can’t offshore these jobs. They must be filled locally.

This is not a cyclical problem. It’s demographic. It’s locked in.

And that’s exactly where humanoid robots come in.

Imagine a robot that works 16 hours a day, seven days a week. At $15 an hour, that’s $87,600 in annual revenue per unit. Multiply that across hospitals, warehouses, farms, and factories, and you start to understand why this is not science fiction. It’s industrial automation at a massive scale, filling roles where humans are no longer available.

Tesla is leading with its Optimus robot, producing several thousand units in 2025 with production scaling to 50,000 to 100,000 by 2026 for internal factory use. Elon Musk believes this could become the most valuable part of the company. Industry reports show that Boston Dynamics’ Atlas robot is deployed at Hyundai’s Georgia manufacturing facility, with plans to scale to thousands of units over the next 5 to 10 years. Figure AI’s humanoid robots at BMW’s Spartanburg facility have already contributed to producing 30,000 plus vehicles, logging over 1,250 hours and loading 90,000 plus parts in just 11 months. This is operational deployment at scale, not concept testing.

Nvidia is right there too. Their GPUs are already essential for AI training, and they’ve launched Cosmos, a new platform designed for embodied AI training. Analysis by Nvidia reveals that Cosmos generates synthetic training data for robots and autonomous vehicles, dramatically reducing the need for expensive real-world data collection. Instead of teaching a robot by having it fail thousands of times in a factory, engineers can train it in simulation. This accelerates development cycles and democratizes access to robotics development for companies that don’t have Tesla’s resources. Nvidia isn’t just a beneficiary of the robotics boom, they’re becoming the essential infrastructure layer. That’s why their stock has exploded.

Ambarella is quietly building the vision chips that help robots navigate and respond to their environments. These chips are essential for perception and safety.

Serve Robotics is already putting autonomous delivery robots on the streets. They’re smaller, but fully operational, and show just how close we are to robotic labor becoming a daily norm.

And here’s the key point.

Figures from venture capital reports show that the smartest money is betting on this convergence. Figure AI raised funding that valued it at $2.6 billion. Toyota is investing heavily in Boston Dynamics. OpenAI is backing physical AI startups. When that much capital aligns with that much economic need, you get secular shifts.

Physical AI is not about replacing humans. It’s about filling in where humans simply don’t exist anymore. It’s about making sure our economy doesn’t break under the weight of retirements, skills gaps, and collapsing birth rates.

It’s a once-in-a-lifetime investment story. I don’t mean I’m buying every robotics stock I see. But I am paying very close attention to this movement. Because the biggest returns usually come from spotting secular shifts early.

BeanFlagShip AutoPilot Investor Update

BeanFlagship is a concentrated growth portfolio built on two things I've tracked obsessively for years: emerging tech and geopolitical inflection points. You can invest directly alongside me, on autopilot, right here.

Invest With Me Now

I launched this fund on the same day growth stocks cratered, and a few weeks before the war broke out. (Great timing!) As a growth stock fund, we took a beating like everyone else in this space. Fortunately, being a geopolitical expert, I had gone long on oil at the same time. It was a small position but it helped stabilize the portfolio through the chaos. I have learned a few lessons that will help us out in the future. Fortunately, we have finally clawed back. Today I am adding five new positions to the fund. Here is why.

Palantir: They just signed a $300 million five-year deal with the USDA to modernize how the government supports American farmers and secure the national food supply chain. This goes way beyond defense. Food security is now a national security issue and Palantir is the infrastructure play behind it.

Vistra: The White House just declared grid infrastructure essential to national defense. Think about what that means. AI is eating the world and it needs power, massive, uninterrupted, always-on power. Vistra sits right at that intersection of energy and national security. This is the AI utilities trade and most people aren’t talking about it yet.

Nike: One of the most iconic brands on the planet is trading like it’s broken. It’s not. This is oversold and we’re stepping in for the bounce. Sometimes the best trades are the simplest ones.

Avex: We sold Ondas and rotated directly into Avex. The defense budget just earmarked $53 billion for drone technology, a 24,000% increase. Drones are no longer a future theme, they’re the current arms race. Avex is our pick to own that moment.

Lemonade: Earnings hit Wednesday. We think this one is set up perfectly into the print and we’re in ahead of it.

See you on Wednesday!

Matt Allen

Good Evening! 👋

Dear Friend,

Happy Wealth Wednesday.

When I look at a company, I always start with one question: who’s running it?

Because no matter how strong the business model looks on paper, the people behind the numbers are what make or break it. Great management teams can turn average businesses into compounding machines. Bad management teams can destroy even the best products in the world.

Over the years, I’ve learned that evaluating management isn’t about charisma or how polished someone sounds on CNBC. It’s about alignment, discipline, and honesty. The best CEOs are not just operators. They are owners, capital allocators, and truth tellers.

There are three traits I look for every single time I research a company:

1. They have real skin in the game. Their personal wealth moves with the stock.

2. They know how to allocate capital. Every dollar earned gets deployed wisely.

3. They are candid and transparent. They tell shareholders the truth, even when it hurts.

These three principles can help you identify leaders who build lasting value, not just short-term excitement. Let’s break them down one by one.

1. Skin in the Game

When you buy a stock, you become an owner. But the real question is, does the CEO see themselves the same way?

The best CEOs don’t just work for a paycheck. They have skin in the game. Their personal wealth rises and falls with the company’s stock price. That alignment changes everything. It turns every decision into something personal.

There’s a big difference between a hired executive and a true owner. When the CEO has millions of their own dollars invested, you can bet they think twice before making risky acquisitions or overpaying for growth.

A study by Institutional Investor found that companies with high insider ownership outperformed peers by 2.3% annually over the last decade. Harvard’s research showed that CEOs who own more than 5% of their company’s stock are 15% less likely to engage in unethical behaviorand have 7% higher employee satisfaction across the board. That’s not a coincidence.

Look at Elon Musk. He owns roughly 13% of Tesla, or about 410 million shares, worth close to $90 billion. That means every operational misstep, every product delay, and every new model launch directly affects his net worth. When the person at the top has that much at stake, they are far more likely to focus on long-term success.

Or take Mark Zuckerberg at Meta. Through a dual-class share structure, he controls over 50% of the voting power while holding a 13-14% economic stake, worth more than $240 billion. That ownership allowed him to pursue big, long-term bets like AI and the metaverse while most executives would have caved to short-term shareholder pressure.

Founder-led companies like these often outperform because incentives are perfectly aligned. In fact, founder-led businesses in the S&P 500 generate 31% more patents and deliver 12% better market-adjusted returns over three years compared to companies run by professional CEOs.

As an investor, I pay close attention to ownership levels. The sweet spot tends to be between 5% and 25%. That’s where the CEO is heavily invested but not so powerful that they can’t be held accountable.

If the CEO owns almost nothing, I lose interest immediately. If they’ve been dumping shares year after year, that’s an even bigger red flag.

When the person running the company has real skin in the game, their goals align with yours. That’s the kind of management I want to partner with.

2. Capital Allocation

Once a company starts making money, the next question is simple: what do they do with it?

Capital allocation is one of the most important skills a CEO can have. Every dollar that flows through a business has to go somewhere. It can be reinvested, used to pay down debt, returned to shareholders, or wasted on poor acquisitions. The difference between those outcomes determines whether your investment compounds or stalls.

William Thorndike, in The Outsiders, outlined five basic options a CEO has for allocating capital:

1. Invest in existing operations

2. Acquire other businesses

3. Pay dividends

4. Pay down debt

5. Repurchase stock

The great CEOs know how to balance those levers better than anyone else. They understand that not every dollar is created equal, and that the price you pay always matters.

One of my favorite examples is Henry Singleton, who ran Teledyne from the 1960s through the 1980s. Singleton built one of the best track records in American business history, compounding at 20.4% annually over 28 years, compared to 8% for the S&P 500. He used overvalued Teledyne stock in the late 1960s to buy 130 companies at cheaper valuations, then flipped the playbook in the 1970s and bought back over 90% of Teledyne’s shares when the stock was undervalued.

He never sold a single share personally. That’s what capital allocation discipline looks like.

A more recent example is AutoZone. Since 1998, the company has spent more than $36 billion on buybacks, reducing its share count by nearly 90%. The result is a 10x gain in earnings per share, achieved by pairing consistent earnings growth with fewer outstanding shares. When done right, buybacks are one of the most powerful wealth creation tools in the market.

The best capital allocators share a few traits. They think independently. They act patiently. They avoid wasteful acquisitions. They rarely issue stock. And they are laser focused on increasing per-share value, not just total revenue.

When I study management, I always look for CEOs who think like investors. The numbers I watch most closely are Return on Invested Capital (ROIC) and Free Cash Flow (FCF). If ROIC consistently exceeds the company’s cost of capital, and FCF is being reinvested wisely or used for buybacks at attractive prices, that tells me leadership knows what it’s doing.

Capital allocation is where vision meets execution. It’s easy to spot companies that grow fast. It’s much harder to find those that grow profitably and know how to reinvest in ways that actually make shareholders richer.

3. Candor and Transparency

The last thing I look for in great management is candor.

It sounds simple, but real honesty in corporate America is rare. Too many CEOs sugarcoat problems, dodge hard questions, or use buzzwords to hide bad results. The ones who tell it like it is stand out because they understand something most don’t: trust compounds just like capital does.

When I read a shareholder letter or listen to an earnings call, I’m not just looking for numbers. I’m listening for tone. Are they being straight with investors? Do they admit mistakes? Do they take responsibility when things go wrong, or do they hide behind excuses?

One of the best examples of transparency in business is Brian Chesky at Airbnb. When the pandemic hit, Airbnb lost 80% of its bookings almost overnight. Chesky didn’t hide. He personally reviewed every layoff decision, wrote an open letter to employees explaining what happened, and walked investors through how the company planned to rebuild. A few years later, Airbnb was back to posting record cash flow. That’s what real leadership looks like.

Another strong example is Jamie Dimon at JPMorgan. His annual letters are long, but they’re worth the read because they’re brutally honest. He talks about regulation, competition, and mistakes just as much as he does success. That kind of candor builds confidence because investors know they’re getting the full story, not just the highlight reel.

As an investor, I’ve learned to respect leaders who admit when they were wrong. It shows self-awareness. It also means they’re more likely to course correct before a small problem becomes a big one.

Now, here’s what I watch out for aka the red flags.

If a CEO constantly blames the economy, regulators, or the industry for poor results, that’s a warning sign. Great leaders take accountability. If every quarter sounds overly optimistic while results keep missing expectations, that’s another one. And if management avoids taking analyst questions or gives vague answers on earnings calls, that’s usually a sign they’re hiding something.

Research shows that 27% of executives admit to manipulating earnings at least once in the past five years. Another 18% said they adjusted operations just to meet short-term targets. That’s why candor matters so much. Transparency builds trust, and trust is what keeps shareholders around when the market gets rough.

When you find a CEO who’s honest about both wins and losses, that’s someone worth investing with. Candor may not show up on a balance sheet, but over time it’s one of the strongest indicators of long-term success.

Conclusion

When I study a company, I never start with the product. I start with the people running it. Because the truth is, great management can turn an average business into a winner, while bad management can destroy even the best idea on earth.

The CEOs I trust the most all have the same three traits. They have skin in the game, so every decision affects their own wealth. They know how to allocate capital, turning profits into long-term value. And they communicate with candor, treating shareholders like true partners instead of outsiders.

When those three things come together, you get companies that compound over time. They survive market cycles, make smart decisions, and keep building even when things get tough.

If one of those traits is missing, that’s when I start asking questions.

Evaluating management isn’t glamorous, but it’s one of the most underrated skills in investing. It helps you filter out hype and focus on leadership that actually builds value year after year.

At the end of the day, the numbers will change, but character doesn’t. And the best investments almost always start with great people.

Cheers!

Matt Allen

Dear Friend,

Happy Wealth Wednesday! We will be launching a new series on Monday’s which talks about some of the emerging trends in tech, ai, growth stocks, and the macroeconomy!

Investing legends like Warren Buffett and Charlie Munger have praised Philip Fisher for his unique approach to stock picking. Fisher wasn’t just looking for undervalued stocks. He was searching for businesses with long-term, sustainable growth. His book, Common Stocks and Uncommon Profits, introduced a powerful checklist that helps investors identify exceptional companies before the rest of the market catches on.

I’ve studied Fisher’s checklist extensively, and over time, I’ve found certain principles to be especially useful in my own investing strategy. These aren’t about quick wins. They’re about finding businesses that can grow for decades.

Today, I’ll break down five of my favorite points from Fisher’s checklist and why they matter when looking for the next great stock.

1. Does the company have products or services with sufficient market potential to make possible a sizable increase in sales for at least several years?

Fisher was all about long-term growth. He wanted to find companies that weren’t just doing well today but had the potential to expand their sales significantly over time. This is crucial because a business that can steadily increase revenue for years, and potentially decades, is where real wealth is built.

Think about companies like Amazon in the early 2000s. It started as an online bookstore, but its market potential was far greater than that. The company had a long runway for growth as e-commerce exploded, cloud computing took off, and digital advertising became a powerhouse industry.

The key here is identifying businesses with multiple ways to expand. Are they tapping into a growing industry? Do they have the potential to expand internationally? Can they introduce new product lines that build on their existing success?

If a company doesn’t have a massive market opportunity ahead of it, the upside is limited no matter how strong the business is today.

2. Does the management have a determination to continue developing products or processes that will further increase total sales when the growth potential of currently attractive product lines has largely been exploited?

A company with a great product today isn’t necessarily a great investment for the future. Fisher emphasized the importance of businesses that constantly innovate and find new ways to grow, even after their initial success.

Look at Apple. The iPod revolutionized the music industry, but Apple didn’t stop there. They developed the iPhone, then the iPad, then the Apple Watch, and now they’re moving into areas like AI and augmented reality. Their ability to create new revenue streams keeps the business growing long after their original products peak.

This is what separates a company with a short-term growth story from one that can compound for decades. If a company is not reinvesting in innovation, whether through R&D, acquisitions, or expanding into new markets, it is a red flag.

As investors, we need to ask: What’s next? If the company’s leadership can’t answer that, it’s unlikely to be a long-term winner.

3. How effective are the company’s research and development efforts in relation to its size?

Innovation isn’t just about spending money. It’s about spending it wisely. Fisher wanted to see companies that invested heavily in research and development, but he also wanted that spending to translate into real competitive advantages.

Look at Nvidia. Their R&D spending has skyrocketed over the years, but it’s not just wasted dollars. That investment has led to cutting-edge GPU advancements, dominance in AI chips, and a moat that competitors struggle to breach. Compare that to companies that throw money at R&D but have little to show for it. Those are the businesses you want to avoid.

A company’s R&D efficiency is a great indicator of whether management truly understands how to allocate capital. It’s not just about the size of the budget. It’s about whether that investment turns into real-world products, better efficiency, and sustainable competitive advantages.

If a company consistently reinvests in innovation and stays ahead of the curve, it’s a strong signal of long-term potential. But if it’s spending on R&D just to check a box, that’s a red flag.

4. Does the company have an above-average sales organization?

This is one of Fisher’s most overlooked but critical points. A company can have the best product in the world, but if it can’t sell it effectively, it won’t matter. Strong sales execution separates great businesses from those that struggle to scale.

Take Salesforce, for example. It wasn’t the only CRM software out there, but its sales team was relentless. They built deep relationships with enterprise customers, executed long-term contracts, and created a recurring revenue machine. The strength of their sales organization helped them dominate the market and build a business worth over $200 billion.

Sales isn’t just about closing deals. It’s about creating a system where revenue keeps compounding, whether through upselling existing customers, expanding into new markets, or locking in long-term agreements. Companies with elite sales teams don’t just grow faster. They often maintain pricing power and defend against competitors better.

If a company has a weak or inconsistent sales strategy, that’s a major red flag. Growth isn’t just about having a great product. It’s about making sure the world knows about it and keeps buying it.

5. Does the company have outstanding management with a long-term vision?

A great business needs great leadership. Fisher looked for companies where management wasn’t just focused on the next quarter’s earnings but was building for the next decade.

Take Jeff Bezos at Amazon. In the early days, Wall Street criticized Amazon for not being profitable. But Bezos had a clear vision. He prioritized growth, reinvested aggressively, and built the infrastructure that turned Amazon into an empire. Investors who understood his long-term mindset made fortunes.

Strong leadership isn’t just about having a visionary CEO. It’s about how management allocates capital, treats shareholders, and adapts to industry changes. Are they making smart acquisitions? Do they return cash to shareholders when appropriate? Are they transparent and honest about challenges?

One of the biggest red flags in investing is when leadership starts prioritizing short-term stock price movements over building a durable business. Great management teams don’t chase trends or cut corners to hit earnings targets. They focus on sustainable, long-term growth.

When evaluating a company, I always ask: Do I trust this leadership team to compound my capital for the next 10+ years? If the answer isn’t a clear yes, I move on.

Final Thoughts Philip Fisher’s checklist is not about finding stocks that will double in a year. It is about identifying businesses that can grow for decades.

The five points we covered today, market potential, innovation, R&D efficiency, sales execution, and strong leadership, are all traits of companies that become long-term compounders. These are not just theoretical ideas. They are the exact principles that have helped investors like Warren Buffett and Charlie Munger build their fortunes.

When I analyze a company, I do not just look at the numbers. I ask myself if this business has the DNA of a long-term winner. If it checks these boxes, it is worth my time. If it does not, I move on.

Great investments are not found by chasing hype. They are found by identifying companies with the right fundamentals, strong leadership, and a clear runway for growth.

Fisher understood this better than anyone, and that is why his principles still hold up today.

Talk to you on Monday!

-Matt Allen

Happy Wealth Wednesday!

Investing is full of contradictions. The more you think you’ve figured it out, the more the market humbles you. I’ve spent years studying markets, analyzing businesses, and watching investors make the same mistakes over and over.

The problem? Many of these mistakes come from misunderstanding some of the biggest paradoxes in investing. Today, I want to break down seven of these paradoxes because once you understand them, you’ll be able to think more clearly, stay patient, and ultimately become a better investor.

1️⃣ The Certainty Paradox

The more certain you are, the more likely you are to be wrong.

Markets thrive on uncertainty, yet time and time again, investors convince themselves they’ve found a “sure thing.” We saw this during the dot-com bubble when people poured money into any company with “.com” in the name. We saw it again in the housing market crash of 2008, where investors believed home prices could only go up.

When everyone agrees something is a sure bet, risks get ignored. And when risks get ignored, markets become fragile.

Smart investors embrace uncertainty. They know that probabilities matter more than certainties. They ask, “What if I’m wrong?” and build a strategy that accounts for multiple outcomes.

Key takeaway: Overconfidence is dangerous. Acknowledge uncertainty, and you’ll make better decisions.

2️⃣ The Patience Paradox

Doing less often leads to better results.

Most people think successful investing requires constant action buying, selling, tweaking. But the data says otherwise. The investors who do the best over the long run tend to be the ones who do the least.

Look at Bitcoin. Since its inception, Bitcoin has had multiple crashes losing 50%, 60%, even 80% of its value at different points. Every time, the headlines declared it “dead.” Every time, people panicked and sold.

But those who held on? They saw Bitcoin rise from pennies to tens of thousands of dollars. Even after brutal drawdowns, Bitcoin has continued to make new all-time highs. The same story repeats itself in every market cycle: short-term traders get shaken out, while long-term holders get rewarded.

Patience isn’t easy. Our brains crave action. But in investing, waiting is a competitive advantage.

Key takeaway: Long-term investing beats short-term trading almost every time.

3️⃣ The Value Paradox

The best investments often never look cheap.

I came from the school of Warren Buffett and Benjamin Graham—the foundation of value investing. Their philosophy teaches us to buy companies trading below their intrinsic value, with strong fundamentals and a margin of safety. And for the most part, that approach has stood the test of time.

But here’s the twist. If a company is growing users, revenue, and most importantly free cash flow at an unusual rate, then it’s probably more undervalued than it looks.

Take Amazon. For most of its early life, investors argued it was overpriced. The company rarely showed a profit, and its P/E ratio was sky-high. But Jeff Bezos was scaling at an unprecedented pace, reinvesting aggressively to dominate e-commerce and cloud computing.

Tesla was the same story. People laughed at its valuation at $50, $100, $200 per share yet those who understood its growth trajectory in electric vehicles and AI made life-changing returns.

The lesson? Value isn’t just about low P/E ratios it’s about the potential for explosive free cash flow growth. Many great companies will always look expensive, but the best investors recognize when the market is mispricing their future.

Key takeaway: If a company is growing rapidly in the right areas, it may actually be undervalued even if it doesn’t look like it on paper.

4️⃣ The Risk Paradox

The thing that feels safest is often the riskiest.

Most people think holding cash is the safest option. No market volatility, no price swings, just a steady balance sitting in the bank. But over time, cash is one of the riskiest places to park your money.

Why? Inflation silently destroys wealth.

A million dollars in 1980 had five times the purchasing power it does today. If you left that money sitting in a bank account instead of investing it, you would have watched its real value get eroded year after year. The same thing happens on a smaller scale every day.

Meanwhile, the stock market, while volatile, has historically delivered an average return of 10 to 12 percent per year over the long run. That is how wealth is built. Not by sitting on cash, but by putting money to work in assets that outpace inflation.

The real risk is not market downturns. The real risk is doing nothing.

Key takeaway: Cash may feel safe, but inflation is eating away at it. Taking measured risks, like investing in equities, is necessary for long-term wealth.

5️⃣ The Liquidity Paradox

The easier it is to sell something, the harder it is to build wealth with it.

Public markets give you instant liquidity. You can buy or sell a stock in seconds with a single click. But that same liquidity can be a curse because it tempts investors to overtrade, panic sell, or react emotionally to short-term market swings.

Meanwhile, some of the greatest fortunes in history have been built in illiquid assets private equity, venture capital, real estate. These investments force long-term thinking because you can’t just cash out when emotions run high.

Take Yale’s endowment fund. Under David Swensen, Yale shifted heavily into alternative investments like private equity, venture capital, and real estate. The result? It outperformed almost every major institutional investor over multiple decades.

Most everyday investors, on the other hand, are trapped in liquid assets. They panic when the market drops, check their portfolios too often, and struggle to hold through volatility.

True wealth is built by investing in assets that reward patience.

Key takeaway: Liquidity is convenient, but it often leads to worse investing behavior. Long-term investors embrace illiquid assets to build lasting wealth.

6️⃣ The Attention Paradox

The more you watch your portfolio, the worse your returns tend to be.

Investors love checking their portfolios. Some do it daily, some even multiple times a day. But research shows that the more often you check, the more likely you are to make bad decisions.

A famous study found that investors who looked at their portfolios frequently tended to overreact to short-term movements. They sold too early, bought at the wrong times, and missed out on long-term gains. On the other hand, those who checked less often and simply let their investments compound did significantly better.

Think about it. If you owned real estate, would you check the price of your house every hour? Probably not. But in the stock market, people do it constantly.

Successful investors understand that more information does not always lead to better decisions. Watching every tick of the market increases stress and creates unnecessary action.

Key takeaway: Checking your portfolio too often leads to overtrading and poor returns. The less you watch, the better you invest.

7️⃣ The Consensus Paradox

The best investments often feel the most uncomfortable at the time.

Most people want confirmation before they invest. They feel safer buying stocks that are widely loved and talked about. But history shows that the greatest opportunities are often the ones that feel the worst in the moment.

Take Amazon in 2001. It had crashed 90 percent from its peak, and most investors had written it off as a failed dot-com. Buying it back then would have felt incredibly risky.

Or look at Bitcoin in 2013. Most mainstream investors dismissed it as a scam or a bubble. Those who saw its potential and held on despite the noise made life-changing returns.

Markets reward those who can think independently. If an investment already looks obvious to everyone, chances are most of the upside is gone.

Key takeaway: Great investments often feel uncomfortable. If you want to outperform, you have to be willing to go against the crowd.

Final Thoughts

Investing is full of contradictions. What feels safe is often risky. What looks expensive can actually be cheap. Doing less can lead to better results.

The best investors learn to embrace these paradoxes instead of fighting them. They understand that uncertainty is part of the game, that patience is a competitive advantage, and that true wealth is built over time—not through constant action, but through disciplined decision-making.

The next time you find yourself questioning the market, ask yourself: Am I falling into one of these paradoxes? Recognizing them can be the difference between being an average investor and a great one.

Cheers,

Matt Allen

Most people think you need a fancy degree or a job on Wall Street to be a great investor.

Peter Lynch? He thought that was nonsense.

He managed the Fidelity Magellan Fund from 1977 to 1990 and turned it into one of the best-performing mutual funds ever.

How good?

An average annual return of 29 percent. That means if you had invested $10,000 with Lynch in 1977, you’d have over $270,000 by the time he stepped down in 1990.

Here’s what made him different. Lynch wasn’t chasing the next big tech breakthrough or trying to outguess the Federal Reserve. Instead, he was buying the businesses people were already using in their everyday lives.

Think about it.

When you grab a coffee, pick up your kid from soccer practice, or stop at the drive-thru, those businesses could be investment goldmines.

One of Lynch’s famous quotes sums it up perfectly. “The best stock to buy may be the one you already own in your backyard.”

And he wasn’t just saying it to sound smart. He put this into practice with stocks like Dunkin’ Donuts, Taco Bell, Pep Boys, and even Service Corporation International, a funeral services company.

The lesson? Investing doesn’t have to be complicated.

Some of the best investment ideas are right in front of you.

But Lynch didn’t stop there. He had rules—principles that guided his investments. And these aren’t the kinds of rules you need a finance degree to understand. They’re simple, practical lessons that any of us can apply today.

Let’s break down seven of Peter Lynch’s most powerful investing lessons. You’ll see how he turned everyday observations into huge wins—and how you can do the same.

Lesson 1: Buy What You Know

Peter Lynch made a career out of turning everyday observations into huge wins. And honestly, that lesson stuck with me from the moment I first read about him.

When I first started investing, I thought I had to be the smartest person in the room.

I figured I needed to find some obscure company nobody else had heard of, dig through hundreds of pages of financial reports, and somehow outsmart Wall Street.

Turns out, that’s not how great investors operate.

Peter Lynch’s approach was so much simpler. He believed that some of the best investment ideas are right in front of you, hidden in your daily life.

Take Dunkin’ Donuts.

Lynch invested in Dunkin’ because he noticed that the stores were always packed. But it wasn’t just about the coffee; it was about the ritual.

People were grabbing their coffee and donuts every single morning before work. It was a habit.

And habits like that? They’re goldmines for businesses.

When I first read that story, I had one of those lightbulb moments. It made me think about all the brands I use without even realizing it.

For Lynch, it wasn’t about chasing the next big tech breakthrough. It was about seeing where people were already spending their money. He figured if a business is part of people’s routines, chances are it’s a solid investment.

So, I started paying more attention to the businesses around me. The coffee shops with lines out the door. The stores I kept seeing on Instagram. Even the products my friends couldn’t stop talking about.

But here’s the thing, Lynch didn’t just stop at “buy what you like.” He always did his homework.

I learned that the hard way. The first stock I ever bought was Sirius XM Radio in the 8th grade due to my parents gifting me $100.

I believed that they would replace CD plays and local radio, but I didn’t bother to look at the financials. Big mistake. The fundamentals were horrible.

Lynch would’ve called me out on that. His rule was simple: Pay attention to the businesses you know, but don’t skip the research.

Think about your own life. What brands do you see over and over? Where are people spending money without thinking twice? That’s where you start.

The next great investment might be sitting right in front of you. You just have to notice it.

Lesson 2: Do Your Homework

It’s one thing to like a product or a business. It’s another thing to invest in it.

That’s a lesson I learned early on, and it’s something Peter Lynch emphasized over and over.

Let me give you an example. Back in the day, Lynch invested in La Quinta Inns, a small regional hotel chain. At first glance, it didn’t seem like anything special. It wasn’t flashy, and it didn’t have a cool story like some tech startup. (Keep in mind that this was before budget franchise hotels were popular.)

But Lynch didn’t just stop at liking the concept of affordable lodging.

He dug into the numbers.

What he found was a scalable, repeatable business model.

La Quinta wasn’t just a hotel chain—it was a well-oiled machine.

Its low-cost operations and steady demand made it a reliable cash cow.

I think about that story every time I get excited about a new investment idea. It’s easy to fall in love with a company’s product.

I’ve been there.

You hear people raving about it, or maybe you’re a loyal customer yourself, and you think, “This has to be a winner.”

But here’s where Lynch’s advice kicks in: liking a product is just the beginning. The real work happens when you look under the hood.

Here’s what I do now, and it’s straight out of Lynch’s playbook. I start by asking questions.

Is the company growing revenue consistently? Do they have a competitive edge? Are they making money, or are they burning through cash? Are insiders buying the stock?

And most importantly, I look for a simple business model. If I can’t explain how a company makes money in a few sentences, I don’t invest.

Think about the brands you love. Now ask yourself—what’s really driving their success? Are they scalable, or are they just riding a temporary trend?

Liking a product is a great start. But without homework, it’s just a guess.

Lesson 3: Ignore the Noise

One of the hardest lessons I’ve learned as an investor is this: don’t let the market’s chatter get to you.

Peter Lynch was a master at this. He didn’t care what the headlines said or what Wall Street analysts were predicting. He focused on the fundamentals of the businesses he owned, not the daily swings of the stock price.

A great example is Pep Boys. At the time, auto parts weren’t exactly a glamorous industry. Most investors were ignoring it because it wasn’t a “hot” sector. But Lynch noticed something that others overlooked—cars were lasting longer, and people were spending more on repairs and maintenance.

While the market was busy chasing trends, Lynch zeroed in on the fact that Pep Boys had a steady customer base and predictable demand. He saw the potential and invested. It wasn’t flashy, but it worked.

I’ll admit, this one hits close to home for me. Early on, I spent way too much time glued to financial news, reacting to every bit of market noise. If a stock I owned dipped because of some headline, I’d panic. If it jumped, I’d think, “Maybe I should sell before it drops again.”

That’s not investing. That’s emotional whiplash.

Lynch’s approach taught me to tune out the noise and focus on what really matters. Is the business growing? Are customers coming back? Is the company well-managed? Those are the questions that matter, not the daily ups and downs of the stock price.

Think about it this way. If you owned a rental property and the real estate market had a bad month, would you sell your house? Of course not. You’d focus on whether your tenants were paying rent and if the property was still generating cash flow. Stocks should be no different.

The market is always going to be noisy. Headlines come and go. Trends fade. But strong businesses with real value? Those stand the test of time.

Lesson 4: Small Companies Can Be Big Winners

Peter Lynch had a knack for spotting small companies with big potential. He wasn’t just interested in the household names everyone already knew.

Instead, he looked for businesses flying under the radar with room to grow.

One of his standout investments was with Pier 1 Imports. At the time, it was a small, niche retailer that focused on unique and affordable home decor.

Lynch saw something that others didn’t: Pier 1 had a devoted customer base and was quietly expanding into new markets.

What set Pier 1 apart wasn’t just its quirky products—it was the company’s ability to scale its business model.

Lynch saw the opportunity for this little retailer to grow into a national chain, and he invested early.

This reminds me of when I first came across Hims & Hers. It was still a small company, but I immediately saw the potential. They offered medicine and wellness products at a discount compared to what you’d normally pay, and the entire experience was simple and scalable.

A business like that has the potential to reach a much larger audience while keeping costs low and customer satisfaction high.

Stories like these always remind me to look past the big names and dig into smaller companies. Small caps often get ignored by Wall Street, but that’s where some of the best opportunities lie.

Think about it. A small company might have a new product, a regional presence, or a unique approach that hasn’t hit the mainstream yet.

When it does, the growth can be explosive.

But here’s the catch. Not every small company is a winner.

Lynch never invested without doing his research. He made sure the fundamentals were solid—strong financials, a clear growth plan, and a product or service that customers loved.

It’s easy to focus on the big, flashy stocks, but some of the best returns come from small, overlooked companies with big ambitions.

Lesson 5: Invest in Unpopular Industries

One of Peter Lynch’s most underrated strategies was looking for opportunities in industries people didn’t want to talk about. He believed that some of the best investments could be found in businesses others overlooked because they seemed boring, uncomfortable, or even unappealing.

A perfect example is Service Corporation International, a funeral services company. Most investors wouldn’t give a second thought to a business like that. It’s not flashy, it’s not exciting, and it’s not the kind of stock you’d brag about at a dinner party. But Lynch saw the potential.

Funeral services are about as recession-proof as it gets. No matter what the economy is doing, the demand doesn’t go away. Lynch recognized that SCI had steady cash flow, a scalable business model, and the ability to consolidate smaller players in the industry.

Think about your own portfolio. Are there industries you’ve written off because they seem too dull or niche?

Maybe those are exactly the kinds of businesses you should take a second look at.

Conclusion: Think Like Lynch

Peter Lynch’s investing philosophy wasn’t built on complexity or insider knowledge. It was about keeping things simple, staying curious, and doing the work.

He taught us to pay attention to the businesses we see and use every day, to dig into the financials, and to think long term. He wasn’t chasing trends or reacting to headlines. He was focused on finding solid, well-run companies with room to grow.

Think about what Lynch achieved by following these principles. He turned ordinary companies like Dunkin’ Donuts and Pep Boys into extraordinary investments. He didn’t overthink it, and he didn’t rely on Wall Street to guide him.

Talk soon,

Matt Allen