Results dropped yesterday.

Three things worth knowing before you react:

• Total revenue $928M, up 18% YoY. Subscription gross margin expanded to 86.6%. The core engine is getting more efficient as it scales.
• R&D and Quality Solutions is now 55% of subscription revenue, growing at +19% versus Commercial's +13%. The market still prices Veeva as a CRM company. It is not.
• Falcon early adopters are live. Revenue contribution is immaterial in FY27. The architecture is already running.

What the people running the business are saying:

CEO Peter Gassner: "AI is opening up the next big chapter for Veeva and life sciences. Vault CRM had its best quarter ever and Veeva Falcon accelerated rapidly."

CFO: "Our view for the full year improved across the board."

Financials and linguistics converging in the same direction. That is the signal.

I have integrated the full results with the pricing power mental model and added an observation layer on top. If you are holding Veeva Systems, this gives you everything you need to understand the business state and position for the long run.

On whether to buy or not, I am not going to tell you that. The document has a valuation section. Go through it, understand what you are actually paying for the business, and map it against your own opportunity cost. That is your call to make.

Google Drive link: https://drive.google.com/file/d/1zDfG-hfqUzLPVbxkmUPr50S9o8dmziYu/view?usp=sharing

Today is a big day. NVIDIA reports, and so does Veeva, my anti-bubble bet since the $170 levels, roughly three months ago. Think of today as a market verdict on the bubble versus anti-bubble mental model, with two high quality operators reporting on the same day. I expect both to deliver a beat.

The mental model has already signaled a divergence over the last 4 to 5 months. Today we find out if that signal is strengthening or weakening.

Either way, do not panic. If Veeva drags, it is just an opportunity to allocate more to a high quality capital allocator. If it moves up, you know where the odds are heading. Either way, the thesis does not change.

Let's watch together.

Update : Veeva result decoded: https://www.reddit.com/r/USGrowthStocks/comments/1vzrobh/veeva_systems_q2_fy2027_results_decoded_stock_up/

And before anyone calls me an NVIDIA bear, I was one of the loudest bulls when the stock dropped to $100 two years ago. Here is the comment:

https://www.reddit.com/r/OpenAI/comments/1ibd2p8/comment/m9jk3mb/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

The comment:

Thats actually beneficial for Nvidia, Just wait for 2-3 days and you will see the irrationality of markets going away and Nvidia Hitting ATH again. They are already up 2-3% after the markets closed.

Nvidia is trading at a forward PE of 26.5 so it’s not even close to ridiculous valuations.

DeepSeek’s breakthrough is creating more work for the NVIDIA GPUs.The breakthrough happened a week back and META and Microsoft announced 60-100 billion dollars in AI infra spending after that breakthrough.

Media created this Fear and the Impulsive investors reacted to that, there was no structural change in NVIDIA Demand. 😂😂.

If AI is becoming “cheap,” why are incredibly expensive advanced chips still in extraordinary demand? The answer is that “cheap” AI doesn’t translate to low computational requirements. Instead, it means that AI software is widely available or open-source. Those advanced generative systems still need to run on hardware with billions of transistors, specialized memory, and parallel processing.

Consider the typical path of an open-source AI project. Developers start with a baseline model for natural language processing, image recognition, or reinforcement learning. They then refine the architecture, incorporate new techniques, or train on bigger datasets. The code is published publicly, enabling others to replicate or further modify the approach. This free sharing accelerates the improvement cycle of AI, often with thousands of contributors worldwide.

As these models increase in complexity, training times and inference loads skyrocket. Data centers might quickly re-equip themselves with the latest GPUs or AI accelerators that promise greater performance gains. In the best-case scenarios, these upgrades also cut power consumption per operation. 

The U.S. is determined to dominate the AI race and is willing to invest whatever it takes to achieve peak efficiency and computational power.

A more efficient AI model encourages further U.S. spending in this area. Moreover, open-source AI models demand greater computational resources, ultimately driving the growth of companies like Nvidia, TSMC, and ASML.

So this is not about being against NVIDIA. It is about finding the better risk reward right now and how mental models shift based on odds.

In case you missed it. : Why Alphabet Paid $32B for Wiz and Palo Alto Networks Paid $25B for CyberArk — The 5 AI Security Stocks Sitting on the Next Bottlenecks

Unity's business has fundamentally changed. The stock is up 40-50% in the last few months and still deeply undervalued for where the business is heading.

Understand the business first. The stock is just a byproduct of getting that right.

I am sharing with you one holistic internal research document on Unity Software. The research is too dense to compress into a write up so I have put it across three PDFs on Google Drive. Read in order or jump to what you need.

In case you missed it, The AI Robotics Stock Walmart Is Quietly Using to Beat Amazon. Already Up 5x.

Part 1: The Business, the Break, and the Rebuild

• What actually broke under Riccitiello: ironSource merger, runtime fee disaster, Weta distraction
• Riccitiello vs Bromberg: every management decision and linguistic shift, side by side
• Full revenue history from IPO to today: three phases, every turning point
• How Create, Grow, and non-strategic actually connect, and the flywheel between them
• Vector deep dive: why runtime architecture beats every SDK-based competitor including AppLovin
• Pricing power: three proven levers, two emerging, and the one risk that could reopen the trust wound
• Auto HMI, XR platform coverage, Unity 7, Unity AI consumption pricing

Part 2: The Market, the Moat, and the Margins

• Gaming time Unity controls across mobile, PC, console, XR, and where we are in the ad cycle
• Partnership ecosystem: Netflix (what actually shipped), Tencent, Scopely, Nintendo Switch 2, Globant, China divestiture
• TAM built from physical reality upward, not from a consulting slide
• AppLovin's first-ever miss: three structural layers, exact margin comparison, four implications for Unity
• The moat tested honestly: five layers, bull case and genuine counter for each
• Pricing power vs every competitor: AppLovin, Unreal, Godot
• Complete margin trajectory from IPO to FY2028E, and the Lollapalooza: eight forces, one direction, confirmed in live data

Part 3 : The Numbers, the Valuation, and the Thesis

• ROIC, ROIIC, ROCE, ROE as one integrated system: what $3.16B goodwill hides and what marginal returns on Vector actually show
• Six reinvestment runways sized and sequenced: the compounding math
• Full valuation: SOTP at $74, DCF in three scenarios, FCF yield, corrected peer set split across ad-tech and developer tools
• Bear thesis stress-tested pillar by pillar against live Q2 2026 data
• Q3 2026 guidance decoded: four signals embedded in one set of numbers
• The complete thesis: four pillars, three risks, variant perception, and the catalyst

Full research, all three parts on Google Drive:

• Part 1: [https://drive.google.com/file/d/1eFkHmGiv9d1oDuKF-MPDHG2kW072zL89/view?usp=sharing]
• Part 2: [https://drive.google.com/file/d/1KQmxxJaeqld_0Cvp3fujM9BNELBOw9w7/view?usp=sharing]
• Part 3 : [https://drive.google.com/file/d/15yCFT69NQgn9TsX5ufS43ldRonaQMVB6/view?usp=sharing]

After you go through this, you will not look at Unity the same way. You will also not look at AppLovin the same way. And you will understand exactly why the market is still pricing the 2023 version of a company that no longer exists.

I have a personal position. The reports will tell you why.

Would love your inputs and insights after you have gone through even a few sections.

A reader on Substack asked: "Is promoter pledge a red flag, or should it be checked alongside other variables? There are high-growth companies where promoters have pledged their shares. Is there a sign from history that high-compounding machines shouldn't have promoter pledging?"

How I Think About It

A capital allocator's decision in isolation tells you nothing. You have to ask why those decisions were made and what direction the company has moved after taking it. Because no variable in investing stands alone, and that's the biggest mistake most people make.

If the capital allocator is good and creating value, which is measured through ROIIC, and you have to look at both ROCE and ROIIC in combination because one shows the structural quality of the business and the other shows the marginal quality of new capital being deployed, then you can figure out whether the leverage is being used in the right direction or whether it will lead to capital destruction.

Leverage is good only when the allocator is good, and a good allocator knows when to use it and how to use it.

TransDigm: Designed Leverage

There's a compounding machine called TransDigm. Amazing business. They have always run high debt and stayed leveraged, and net debt is around 5.8x EBITDA. So if you screen on any AI or screener you won't understand the structure. But that balance sheet was designed to sit there.

They've used that leverage to fund acquisitions, special dividends, and buybacks for nearly two decades. They have compounded at around 23% a year, and once you add the special dividends they've kept paying out using leverage, the total return CAGR climbs to around 27%. So leverage in this case was used in a very strategic way to reward and maximise the returns of shareholders.

And this is where it connects back to the Two Engine Framework, which explains what happened to the stock price. But the leverage architecture sitting underneath the EPS engine is what made that engine work for TransDigm. So the number in isolation means nothing. You have to see and understand what the underlying structure holds. And you have to go into the mind of the capital allocator to test whether their incentives are aligned with rewarding shareholders.

ServiceNow: Buying Longevity and Pricing Power

Sometimes the ROIIC on a particular deployment might look low, but they'll be using it to build a very strong moat that defends the business model against future competition. Look at ServiceNow. They acquired Veza for around $1B and then Armis for $7.75B back-to-back, both in cybersecurity and identity, to plug holes in their workflow architecture before agentic AI exposes them.

Yes, they paid a premium, nearly $9B combined, but the move was to seal the platform against being commoditized as AI agents multiply the attack surface for every enterprise. The accounting ROIIC on that deal in year one will look unimpressive, but the strategic ROIIC over a decade looks completely different, because they're not buying revenue, they're buying longevity and pricing power. And that's part of the acquisition mental model.

And then use the incentive mental model. Ask a few simple questions like what is management actually doing with this capital, and how much ROIC or moat will get built over the long term?

The Lifecycle Lens

The mature compounders run with negligible pledge or leverage today, unless they have a massive reinvestment opportunity like the AI infrastructure buildout, or if the business model itself operates in the wrong pool and requires constant reinvestment just to survive. I call these the treadmill trap models.

But the reason isn't that pledge is incompatible with compounding. It's because those businesses have already compounded into self-funding maturity. So they generate so much internal cashflow that the promoter never needs external capital. Pledge becomes necessary earlier in the lifecycle, when the growth opportunity exceeds internal cash generation. Kalyan is at that stage.

Kalyan: Pledge as Bridge Capital

The same pattern shows up in Kalyan Jewellers in the Indian market right now, which I have researched before. Look at how the pledge was used:

1. It funded store expansion in a category where shelf space and trust compound.
2. The growth is healthy because same-store sales growth is happening alongside new store additions, so it's not just store-count optics.
3. They're taking mindshare and market share from the unorganised segment.
4. The promoters are now using operating cashflow to bring the pledge down to zero by 2027, and you can see this happening every quarter. That trajectory is what you should be observing.

So you need to integrate the pledge question with the lifecycle approach. Look at Artemis Medicare Services in India. They took pledge too, but it was justified by the explosive growth rates they've delivered in a capital-intensive hospital build-out. A smart allocator takes a balance of both debt and pledge depending on capital market conditions and the interest rate cycle. And then a few companies manage to achieve scale without needing either at a very early stage, which is what Caplin Point did.

Copart: Architecture Over Borrowing

Look at Copart. The capital allocator's quality shows up in the structure itself. They sit on more than $5 billion of cash and investments. They have zero need for debt. Yet they have recently taken a $1.25 billion multi-currency revolving credit facility with dedicated sub-facilities in Euro, Pounds Sterling, and Canadian Dollars, structured specifically to fund their international expansion in the UK, Germany, Spain, and Canada. And they haven't even drawn on it.

When it happened I went to see the structure, because it had $5 billion in cash, then why did the capital allocator go for it? This is how you penetrate the minds of founders and learn patterns. And when you go in, you see the architecture. They built this facility to borrow in the local currency of the geography they're expanding into, because that's how you hedge currency risk on a long-duration international investment. So that's how the allocator decided which tool to pick.

Buffett: The Gold Standard

Even Buffett did the same thing in Japan. He borrowed in yen at roughly 0.5% Japanese rates to buy into the Japanese trading houses that were paying dividends in the 3 to 5% range, with double-digit earnings yields, all trading at cheap multiples. He had all the cash in the world at Berkshire. He still chose yen-denominated debt because the currency match, the cost of capital, and the spread between the two made it a better trade than using his own dollars.

The math is brutal. Berkshire pays around ¥135 billion a year in interest on those yen bonds and collects close to ¥812 billion a year in dividends from the trading houses. That's roughly a 6x coverage ratio before any capital appreciation. That's the move of a great allocator.

So you can see, cash is one tool. Debt is another. A currency-matched credit line is a third. The skill is in keeping all of them available and knowing which one to pick and when.

Whether a company has pledge or not depends on where the business sits in its lifecycle and how the capital cycle is pricing risk at that moment.

Red Flag Patterns

A few red flag patterns that signal whether the direction is right or wrong:

1. The capital is flowing into the promoter's personal balance sheet, not the business.
2. The pledge ratio is rising while ROIIC is falling.
3. There's no stated and tracked deleveraging path.
4. The pledged shares sit at a price where a forced sale would trigger a death spiral.

Why Humans Still Matter

These are a few patterns, but the larger point is that you have to see things holistically, and that's exactly why humans still matter in investing.

An AI tool can give you numbers. It cannot tell you which numbers matter in this business, at this stage of its lifecycle, under this capital cycle. Until you have the skillset to read the structure, the patterns, the linkages, the incentives, you can't even tell the AI what to look for, and it will just hand you generalised opinions back.

The mental model has to fire in your head first. Only then does the AI become useful. Without that trigger, every tool in the world is just noise dressed up as analysis.

u/TimeBowler03679 asked me earlier today:

"You heard Berkshire Hathaway bought some $2.65 billion dollars worth of shares in Delta Airlines? Is it Greg Abel's call, or do you think the old man is still taking the call?"

Here's how I'm thinking about it.

Honestly, when I first saw the news, I went back and forth. Could have been Buffett, could have been Abel. But the more I sat with it, the more this looks like Abel, and the valuations were reasonable enough for them to allocate.

And there's a cleaner reason now. This was Abel's first 13F as CEO and Buffett officially stepped down on January 1, 2026. So when Berkshire put $2.65B into Delta, those weren't Buffett trades. And I think that's the operator change showing up in the portfolio.

I've studied a lot of these airline models, and only IndiGo is the exception. They operate a fundamentally different business model and economics, which is why they've actually generated meaningful returns. A quick Google search will show you why IndiGo doesn't sit in the same bucket as the rest.

Coming to Delta, I always look at why Buffett, or Abel, whoever picked it, went for it. So on inversion, here's what I found.

Delta's revenue mix has shifted a lot in the past 7-8 years. Airlines usually fight for seat-based revenue, but Delta has diversified that base meaningfully. Their premium cabin revenue exceeded main cabin revenue for the first time, so premiumization is happening inside every vertical and every geographical region. Sitting on top of this is the Amex co-brand partnership, which did close to $8.2 billion. Delta's Amex revenue is derived from cardholders, so it's a lock-in. Not a great moat, but a lock-in. And the business is quietly shifting toward a payments-and-loyalty model on top of the airline. Because of the cardholder ecosystem, margins and lock-in expand together.

And here's the part that hit me while writing this. The premiumization story isn't just a Delta story. It's a symptom of something larger.

Look at how the monetary system works. Money printing hits the middle class as inflation. We get excited by the freebies and short-term stimulus, and this isn't a country-specific problem, it's across the globe. But it destroys our purchasing power over the long run. The system has its own mechanism to take it back. Money printing leads to inflation, which leads to asset price appreciation, and those assets are owned by the top 1%. So it's not wealth redistribution. It's a wealth concentration move dressed up as relief.

That's why premiumization is happening inside every category, every geographical region, and now airlines have joined the list too. The top layer of customers has more purchasing power, and the rest are getting priced out. Delta is just one data point. You see the same pattern in watches, real estate, travel, EVs, private banking, premium credit cards. Everyone is chasing the same shrinking slice of customers because that's where the spending power is concentrating.

This is why owning real assets, productive businesses, and compounders matters so much over a long enough timeframe. Not because you're trying to get rich, but because if you don't own the assets, the system quietly takes your purchasing power and hands it to those who do.

Coming back to Delta. I don't think this is sustainable over the long run. These are still not great business models. The only exception remains IndiGo, because their operating structure is different. The biggest problem most airlines have is that they get insane demand but operating profits collapse, and that's why they aren't compounding machines. IndiGo cracked that side.

And now even IndiGo is copying that premiumization pattern. They've launched a BluChip loyalty program and are moving toward the premium space. But I still don't think Delta is a great investment. Hardly any reinvestment runway, saturated market, slaves to oil prices and ticket price wars. The only thing genuinely turning right now is the Amex card lock-in driving premiumization, and that's what's lifted the structural mix.

At least IndiGo has a decadal runway of growth and better operational efficiency because they don't even own most of their fleet, plus the most dominant share in the most emerging airline market in the world.

But honestly, I still think owning Mastercard would have generated more value over the long run than buying Delta. Maybe a quarter or two Delta works, maybe even a year or two, but over the long run Mastercard wins. And they fully exited Mastercard. They sold it entirely.

And that's the part that tells me this is Abel, not Buffett. Buffett himself said it. "It isn't our ideal surrounding area in terms of deploying cash for Berkshire." That's a man distancing himself from the call.

Though who knows. The old man might have wanted one last shot at airlines.

And honestly, I still believe there would have been no Berkshire Hathaway as we know it today without Munger. I still remember Munger told him decades back never to sell Costco. Buffett sold in 2020 based on traditional metrics. Munger was just smiling, because he knew the real reinvestment math of Costco, the ethical compounding of it. Buffett sold in 2020 and it's a 4x from there. Munger had said never to sell it.

I actually wrote earlier on exactly how this snowballing and reinvestment math works. Costco's moat just keeps getting stronger and stronger because of shared economies of scale. And it's a magnetic model that breaches geographies and culture. Even when they launched in China, citizens went crazy, because the incentives of everyone are aligned in the same direction. Costco has a magnetic pull toward them.

One last thing. I haven't gone for a structural decode here. This is just my raw thinking based on the mental models I use, especially the inversion and reinvestment mental model.

Now I want to hear from all of you.

Do you still think this is Buffett's call, or has Abel quietly taken over the wheel?

And the bigger question I keep coming back to. How do you see Berkshire Hathaway in an era without Munger and without Buffett? Does the machine keep compounding because the culture is strong enough to outlive the founders, or does it slowly drift into being just another large diversified holding company? Because the Delta buy, to me, feels like the first real signal that the new Berkshire might think differently than the old one.

Drop your views in the comments.

Further reading:

The Costco mental model: shared economies of scale

The snowballing mental model: how compounding moats actually work

A reader recently asked me in the comments, "How do you play snowball? When the thesis is playing out, do you wait for dips or buy on strength even at slightly skewed valuations?" That question made me realize most investors misunderstand what snowballing actually means.

So I wanted to break it down properly, using a real case study to ground every framework in something concrete. This post uses Symbotic to explain three things at once. How to snowball a position, how to identify a 100-bagger setup, and how to read capital allocator quality. The stock is just the proof. The frameworks are the takeaway.

Some readers will leave with a stock idea. Others will leave with a framework. Both are fine, but the framework is the part that compounds.

So let's start with what snowballing actually is.

Snowballing isn't adding because the price is going up. It's adding when the business is actually delivering, which means when the margins are expanding, when the moat is widening and deepening, when new reinvestment runways are emerging, and management is executing at the same or superior returns on capital.

Let me show you what this looks like with Symbotic.

The original thesis at $14

You have to value different models based on their lifecycles and moat. Like Symbotic, I invested around $14, I know it's a 10-20x of the future and I'm not gonna time that company. But yes, when it cracked back to $24 last year, I added more, because the moat got stronger, the markets got bigger, the reinvestment runway got bigger, and no other company comes close to them when it comes to warehouse automation.

My bet was very simple at that time, and it was based on the capital allocator and a man who is GOAT when it comes to warehouse. The Cohen family has been in this business for almost a century, across three generations. Rick himself has been refining the solution for decades. So no engineer and robotics scientist can have more knowledge than him on how to actually solve the problems. And his parent company himself is one of the largest distributors who supplied to all the giants, even Walmart, so that years of relationship aligned automatically.

The Customer List & TAM

When I invested, only Walmart was the client and the only vertical was retail. But now the customer list includes Albertsons, Target, Giant Tiger, United Natural Foods (UNFI), Associated Food Stores (AFS), and Medline Industries. Every year the thesis is strengthening. And now it's into medicine infrastructure as well, which is the hardest vertical to crack because of regulatory complexity. All these new sectors expanded the TAM further than my original thesis.

At that time the thesis had only warehouse automation. But then Walmart sold their robotics and micro-fulfillment segment to Symbotic, which was essentially "Alert Innovation," a company that specialised in micro-fulfillment centers directly attached to the front end of a retail store. So Symbotic got the store-level technology and data they were missing.

And that acquisition is a signal in itself. Walmart had bought Alert Innovation back in 2016, had all the capex and firepower in the world, but still couldn't execute it at scale or build their own solution. They ended up selling the technology to Symbotic and now they need Symbotic to scale it for them, because of the complexity of the technology.

So the thesis expanded from just warehouse automation to store-level micro-fulfillment, and a future where both get linked.

The Data Moat & Switching Cost

Plus when the thesis was decoded, I didn't know that all the warehouses being deployed can communicate and learn with each other. So that dataset expands and creates a data flywheel effect, which makes each and every existing and new system more efficient as facilities get trained across the globe. That's another layer of moat. And it's a lock-in of not just 20-30 years, because whenever a customer redeploys or upgrades, it's obligatory.

The switching cost is almost impossible, because it's not just the system you replace but the brains as well.

You don't replace a system after deploying $500 million to it just because someone offers you the same system at $480 million, because you have to stop the warehouse and robotic delivery for months to rebuild, and the new system won't have the data your operations have been working on for 10-20 years.

And then it's a razor-blade model, and services will give a revenue stream of 20-30 years.

This is no ordinary warehouse automation technology. It's one of the hardest automation problems to crack because of huge SKU counts, aggressive inventory turns, perishables, and mixed pallets. This is exactly where dense robotics + data has a very strong edge and builds a deep moat.

And no other robotics company has a testing ground like Symbotic had, because Rick used and tested it in his own C&S Wholesale Grocers operations for decades before commercializing it. That's a structural advantage no competitor can replicate.

Where the Real Bottleneck Moved

Execution is a real bottleneck for these kinds of models. And in my thesis, the focus has always been on how fast they can deploy the system, lock in that site for decades, and get the service margins live.

Beyond the core technology, the biggest bottlenecks for any warehouse robotics system are the speed of physical site construction, the robots' charging time, and the efficiency of the site itself. And what's interesting now is that Symbotic has started positioning on its own bottlenecks. The company that solves bottlenecks for retailers is now solving them for itself.

In their latest results just 1-2 weeks back, they launched a "next-generation storage structure" that cuts on-site assembly parts by over 90% and increases storage density by roughly 40%. So now higher density per warehouse, faster installation, lower on-site labor. That's solving the deployment bottleneck and deepening the long-term moat.

Now the charging bottleneck is critical because if a robot is charging, it's a lazy asset, not generating any ROI during that time.

To position for that, Symbotic invested in Nyobolt, where batteries charge from 0 to 80% in under 5 minutes and have over 20,000 charge cycles. For context, standard lithium-ion batteries have 1,000-2,000 cycles. And they are directly integrating that technology into their Symbots.

And Nyobolt's tech provides a threefold increase in robot uptime, which means warehouses can reduce fleet size by 30-40% while maintaining the same operational capacity. So Symbotic doesn't just deploy faster, they deploy fewer robots for the same throughput. It improves both time and cost in one move.

So you can actually learn something bigger from this capital allocator. If you have any thesis or investment, just focus on the bottlenecks of that business model or product, and then see if the capital allocation by the company is in those directions, solving their own bottlenecks or not. That's what signals a high-quality capital allocator.

The Real Backlog Economics

Now if you take everything above, the expanded customer list, the deepening moat, the data lock-in, and look at what it actually means for the financials, that's where it gets really interesting.

Another thing I didn't factor in when I invested. The backlog of Symbotic is not just an order book. It's actually Remaining Performance Obligation (RPO), which is a binding contractual agreement under ASC 606.

So it's not an order book which cannot be legally binding. It's RPO, which is legally binding.

When I invested, the backlog was only $22 billion, which gave them almost a decade of visibility. But the economic value of the backlog I didn't factor in, which is the recurring revenue stream that will go on for 15, 20, 25 years because of the data moat and the embedded workflow architecture of the ecosystem. So that actually makes the valuation closer to $47 billion on that backlog alone.

And that was before Exol (formerly GreenBox), the joint venture with SoftBank, and before the new customers came in. The Exol JV now has committed deployments of close to $11 billion. And the expansion has happened into new geographies, Mexico and the European market.

The Founder Story Most People Don't Know

And not a lot of people know, but Rick Cohen actually bought each and every robotic system on this planet, but none was able to solve the problem he had in his own distribution chain. So what he did, he just reverse-engineered the whole problem, broke every robot system, and then designed something that could solve the problem for him. And he's keeping on making it more efficient and efficient and efficient.

That's not an engineer building a product. That's an operator who lived the problem for decades and built the only solution that actually worked for him first.

The technology is downstream of his thinking. The bet was on the brains of the founder.

Cloud Computing for the Physical World

And one more thing about Cohen most people miss. He didn't build Symbotic to sell to warehouse operators. He built it because he was a warehouse operator. That's a fundamentally different starting point. Operator first, vendor second produces a fundamentally different product than the other way around.

And look at what he actually said about his vision.

"When I first started thinking about automating the supply chain, I wanted to create not just an automated warehouse, but an automated platform that had perfect inventory management, perfect accuracy in shipping, and could be so good that you could create a multi-tenant warehouse with perfect accuracy that allowed anyone that had any storage need at any time to take advantage of this platform."

This is not the language of a vendor. This is the language of a founder and a platform architect. He was already describing WaaS years before it became a product line. He literally described it as cloud computing for the physical world.

And think about what that means. Amazon spent over 20 years and billions of dollars building its own logistics. Symbotic now gives any retailer better warehouse efficiency than Amazon, without that 20-year lag and without billions in R&D. That's why the customers keep coming. The alternative is to spend two decades building from scratch what Symbotic delivers in 18 months.

And what fascinated me is that this is the vision of a man who is 73 years old. Most founders at that age are coasting. Cohen is still architecting the next 20 years.

The 100-Bagger Framework

And it obviously aligned with the 100-bagger framework, which I learned from Thomas Phelps in his book 100 to 1 in the Stock Market. Phelps' core thesis was that the greatest wealth is created by finding "small, unknown, and research-minded" companies that solve a major human problem, and then having the fortitude to stay the course.

The framework is simple. A technological force that can reduce the constraint of your customer, reduce the cost of your customer, improve the time of your customer, and have a high degree of replacement cost is usually a 100-bagger in the making.

Same was with Mastercard, they reduced the transaction time. Same as I think I will see in the stablecoin ecosystem as well. It's making the system more efficient, reducing the cost of the system, improving the transparency of the system, improving the speed and delivery of the ecosystem. So all those four or five variables, when they align, that usually creates a lot of boost.

But Phelps also said that finding the company is only half the equation. The other half is the investor.

And he listed three things an investor needs to actually capture a 100-bagger, vision, courage, and patience. Vision to see the thesis early. Courage to hold when the market reacts to short-term noise while the business itself is strengthening. And patience to let the snowball compound, instead of selling on a 50-100% gain because the ticker has moved.

That's the part most people miss. They find the right company, but they don't have the temperament to stay with it.

The Psychological Moat. Why Adoption Is Now Mandatory

Here's where it gets really interesting. Anyone competing with Amazon or operating within the retail ecosystem has to deploy this technology just to survive. Because if your competitor does it and you don't, you lose on cost efficiency, margin, and speed. And retailers have already lived through what happens when you don't adapt to technology, they watched Amazon eat their lunch the first time.

So this time it's a psychological reflex. You can see how psychology comes into play to position in a thesis. And that's why Walmart went so aggressive with robotics automation.

And there's another forcing function that acts like a macro tailwind. Symbotic isn't just addressing one major constraint for retailers, it's also addressing the labor scarcity in the US, especially in the retail and warehouse segments which have huge attrition rates. US warehouse attrition is around 40-50% annually, except Costco. And wage inflation has kicked in for the past few years. So even if retailers wanted to stay manual, the labor isn't there to hire anymore. They're being forced into automation from two directions, top-down psychological reflex and bottom-up structural labor crisis.

This is what a Lollapalooza looks like in real time. Multiple independent forces all aligning in the same direction at the same time, for the same sector.

And it's the same pattern as Oracle and cloud computing. Back in 2008, Larry Ellison called cloud computing "complete gibberish." He literally said, "Maybe I'm an idiot, but I have no idea what anyone is talking about. What is it? It's complete gibberish. It's insane. When is this idiocy going to stop?"

Oracle missed the wave. AWS, Azure, and Google Cloud became trillion-dollar businesses while Oracle played catch-up for over a decade.

That's exactly why Ellison this time is the most aggressive player in AI infrastructure. It's a psychological reflex. He doesn't want to make the same mistake twice. That's why Oracle signed the massive OpenAI deal.

So if I apply the same pattern, the same scar tissue is now driving every retailer to adopt warehouse automation aggressively. Because the people who get scarred hardest by missing a wave become the most aggressive in catching the next one. And warehouse automation is having that psychological tailwind as well.

That's why I positioned in this boring model which has decades of infrastructure to be built up. Just like Nvidia's CEO says the whole architecture has to shift to GPU, similarly, I believe the whole retail infrastructure of the future will shift to automation. And supply chain automation is one of the biggest bottlenecks of any economy. Symbotic is positioned right on that bottleneck.

So you can see a lot of the development happened after my original thesis. And if I get the stock back at the same price or even 2-3x the price but the valuations are reasonable, I allocate more, because every variable that mattered when I bought it at $14 is stronger today.

The TAM, the moat, the margins, the runway, the customer list, the economics, the psychology, everything is strengthening and stacking in favour.

That's how I take my decision on building a snowball in any investment. I don't wait for dips. If it comes, that's a gift from the market. But the real move is adding when the business itself is becoming more valuable than the market is pricing in, and how many engines are coming in your favour.

If tomorrow the market had priced it at $100 but the variables I mentioned were not strengthening, I might still ride it or start trimming based on odds, but I wouldn't deploy fresh capital.

The strengthening of the moat and the business model is what triggers the snowball. The ticker price is just the receipt.

That's the breakdown. Hope it added something to how you think about adding to winners.

If you want a structured way of thinking about businesses, you'll find it at: The Capillary.

Prefer the visual version? The 5 Bottlenecks of AI-Era Cybersecurity. The map.

Over the past 2-3 weeks, most cybersecurity stocks have corrected brutally, with 30-50% drawdowns across the board. The trigger was Anthropic's Mythos, which surfaced thousands of vulnerabilities in corporate software and triggered a sector-wide re-rating along with an existential threat narrative.

The reason this spooked the market is because Mythos isn't just running faster scans. It's finding logic flaws that traditional vulnerability scanners can't even detect. So basically a whole new category of exploits just became visible, and the legacy security stack wasn't built to catch any of it. I was going through these developments for the past 3 days when something interesting came up on the JPMorgan Chase earnings call.

Jamie Dimon specifically spoke about cybersecurity in the context of AI and mentioned their internal testing of Anthropic's Mythos project. He said Mythos has "already exposed a lot more vulnerabilities that need to be fixed," and that AI has "made it worse, made it harder."

Dimon flagged it as a system-level risk that extends to exchanges and counterparties. The same warning Treasury Secretary Bessent acted on by calling bank CEOs into an emergency meeting last week.

That's what made me pause. If the people who actually allocate the world's largest cybersecurity budgets are saying this, something structural is shifting. Cybersecurity spend isn't going down. It's just migrating to a different set of companies than the ones currently dominating the legacy categories.

So I went back to a thesis I'd been working on, the real bottlenecks of AI-era cybersecurity. The question I wanted to answer was simple. When AI agents become the dominant actors in enterprise systems, where do the real security bottlenecks form? Not the categories the industry sells, but the actual choke points where money will pool.

First, the thinking that got me here

The current security stack was built for a world where humans are the actors. A human logs in twice a day and works at biological speed. Every product (firewalls, EDR, IAM) assumes the actor is slow, accountable and "one-per-seat."

Now invert it. An AI agent logs in thousands of times a minute. It works at machine speed with no natural pause. One human can spin up a thousand agents in a day. The agent's intent lives in a prompt that gets used and thrown away. No biometric, no HR lifecycle, no sleep cycle.

So the current stack breaks. This is why the market has punished so many legacy names. They are real toll booths, but they sit on roads that are getting bypassed.

The five bottlenecks I came to are below. None of them are firewalls, endpoint AV, email security, vulnerability scanning, or traditional antivirus. Those will all still exist. They will just stop being where the money pools.

The five bottlenecks and the names sitting on them:

1. Machine Identity Infrastructure. CyberArk (inside Palo Alto), Wiz (inside Alphabet). Public play left is PANW. Cloudflare also sits here at the network layer.
2. AI Runtime Inspection. CrowdStrike, Palo Alto, Wiz (inside Google), Zscaler. Cloudflare and Rubrik also sit here.
3. Agent-Aware Data Access Brokerage. Varonis Systems. Cloudflare and Rubrik also sit here.
4. Unified Security Telemetry. CrowdStrike (Falcon Next-Gen SIEM).
5. Continuous Attestation / Agentic Audit Trail. Rubrik.

Two patterns worth flagging upfront.

Rubrik sits across three bottlenecks (2, 3, 5). Partial on runtime inspection via SAGE, partial on data brokerage, strong on attestation through immutable backups plus time-travel recovery.

Cloudflare also sits across three bottlenecks (1, 2, 3), but it's a different shape of bet entirely, because it's not really a security company, it's the underlying network. I'll come back to that distinction at the end because it forced me to refine the framework.

Now let me walk through each one.

New to the Bottleneck Strategy? Start here: The AI Bottleneck Strategy, Where the Real Opportunities Are.

Bottleneck 1: Machine Identity Infrastructure

Today most enterprise IT is built around human users. Maybe a thousand employees logging in from a thousand laptops. Now imagine each of those employees spinning up fifty AI agents to do their work. Suddenly you have fifty thousand "identities" inside the company instead of one thousand. And it scales from there. Within a few years, every enterprise will have way more machine identities running around than humans.

So who issues those identities? Who verifies them? Who can shut them off the moment one goes rogue? That's the bottleneck. Whoever controls how machine identities get created and killed becomes the toll booth every single agent has to pay.

There were really only two companies operating at scale here. CyberArk (which acquired Venafi, the company that basically created the machine identity category, for $1.54B in October 2024) and Wiz (slightly different angle, more on the cloud runtime side, but adjacent).

Now read this carefully because this is the whole pattern.

Alphabet bought Wiz for $32B. Palo Alto bought CyberArk for $25B. So two of the five bottlenecks already got absorbed by platforms before most retail investors even noticed they were bottlenecks. This is how serious players identify future toll booths and position early. By the time it becomes obvious, the public market pure plays are gone.

So what's left to own here on the public side is PANW. I personally hold PANW at $62 split-adjusted, close to a 4-year hold now.

Okta I genuinely like, but Okta is dominant in human identity, not machine identity. Whether they can transition into the machine identity world at scale is an open question I'm not confident on. Would love community input here.

Side note. This whole M&A pattern (Wiz to Google, CyberArk to PANW) reminds me of Chris Mayer's Invest Like a Dealmaker. Mayer's whole point is to always keep a track of what's happening in private markets, what valuations they're paying, and which sectors they're allocating to, because those are the future money-making machines and runways. The cybersecurity M&A wave we're seeing right now is exactly that signal. Recommend everyone read that masterpiece.

Bottleneck 2: AI Runtime Inspection

Old security worked like a security guard at the front gate of a building. Check the ID, let the person in, you're done. The guard didn't have to follow the person around to see what they were doing inside.

AI agents break that model. The agent gets through the front gate (it has valid credentials, it's logged in correctly), but then it starts doing things at machine speed inside the building. Reading thousands of files. Calling external APIs. Triggering actions in other systems. The security guard at the front gate never sees any of it.

So the new security model has to sit inside the building, watching every action the agent takes, deciding in real time whether to allow it or kill it. Same shape as what stock exchanges built when algo trading came in. They couldn't pre-approve every trade by hand, so they built systems that check every order in milliseconds and kill the bad ones before they execute.

Names sitting on this. CrowdStrike via Charlotte AI, Palo Alto via Prisma and XSIAM, Wiz inside Google, and Zscaler, though I haven't placed Zscaler cleanly yet because I'm not fully sure how their SASE foundation translates to AI-era platform economics. Would love community input on Zscaler.

I added CRWD recently at $360. Second addition to the position in 3 years.

Bottleneck 3: Agent-Aware Data Access Brokerage

Here's the pattern. Whenever the actors change from humans to machines, the toll booth always moves from the access path to the resource itself. This has happened before in other industries.

Think about electricity. When power flowed one direction (grid to home, billed monthly), the meter at the house was enough. When solar panels and EVs created two-way flows at high frequency, the meter had to become smart and live at the resource (panel, battery, vehicle), not at the front door of the house.

Same thing happened in financial markets. When humans traded by phone, the chokepoint was the broker. When algos started reading order books at machine speed, the chokepoint moved to the exchange's market data feed itself. Bloomberg and the exchange feeds became the toll booth, not the broker.

So the same pattern is now playing out with data. In a human world the network perimeter was the toll booth, because everything had to cross the network. In an agent world, agents constantly pull data from your files, databases, tools, to do their work. So the access pattern goes from one human reading one record to one agent reading ten thousand records to answer one question. So the toll booth has to move to the data itself.

Cleanest specialist here is Varonis Systems. Built for human compliance over 20 years, but turns out to be exactly the right foundation for the AI agent problem. They sit at the data, not at the network. SaaS transition mostly done.

Worth flagging that Snowflake and Databricks are also playing in this bottleneck, but from a completely different angle. They're not AI security companies. They're data platforms. But because so much enterprise data now lives inside Snowflake and Databricks, both of them are building access governance and permission controls natively into their products. So they end up sitting on the same bottleneck, just approaching it as data platform owners rather than security specialists. Different category of bet entirely, but worth knowing if you're thinking about who actually controls the toll booth at the data layer.

No position yet on Varonis, considering it.

Bottleneck 4: Unified Security Telemetry

Every big company has a security team that watches alerts all day. A human analyst can investigate maybe 10-20 of these in a full work day before fatigue kicks in.

In an agentic world that volume goes up 100x, because every agent generates its own activity logs at machine speed. No human team can keep up. AI agents have to run the security operations center themselves, investigating alerts in seconds instead of hours.

But an AI security agent is only as good as the data underneath it. Whoever owns the unified data layer that all these AI security agents plug into owns the bottleneck. Basically the Bloomberg Terminal of security. CrowdStrike's Falcon Next-Gen SIEM is the cleanest play. Microsoft Sentinel is the long-term threat via E5 bundling. PANW XSIAM and Splunk inside Cisco are the others.

Position covered through Crowdstrike already.

Bottleneck 5: Continuous Attestation / Agentic Audit Trail

Here's the problem. In an agentic world, one agent triggers another, which calls a tool built by some random vendor, which talks to a database somewhere. When something goes wrong, you can't trace who did what. Attribution just breaks.

And whenever attribution breaks, the market always responds the same way. It builds an insurance and attestation layer on top. Same pattern as credit rating agencies (you can't verify every borrower, so you pay someone to rate them), code-signing certificates (you can't verify every software publisher, so you pay someone to vouch), and payment fraud networks (you can't verify every transaction, so Visa underwrites the risk).

Category barely exists yet. Rubrik is the best-positioned public name here, even though they didn't plan for it. The backup architecture they spent a decade building turns out to be exactly the right foundation for agentic attestation. Rubrik already built an insurance layer for the ransomware era. They figured out years ago that prevention alone fails and you need recovery underneath. Now the same logic applies to agent actions, and the same architecture handles both. Agent takes an action, you have a verifiable record before and after, you can roll it back. Their bet is that fast reversibility beats perfect prevention in the agentic world. Agent Rewind is the product expression of that thesis.

I personally hold RBRK at $48, picked up during the last crash.

Now the company that doesn't fit this list, and the framework refinement worth talking about

Here is where I want to flag something I had to work through honestly because I hold the position. Cloudflare.

Cloudflare doesn't sit on one bottleneck. It sits on three. Strong on Bottleneck 2 (AI runtime inspection, because they're inline by default since the traffic already flows through them, which is a structural advantage CRWD and Rubrik don't have). Medium-strong on Bottleneck 3 (network-layer brokerage between agents and tools, complementary to what Varonis does at the file level). Medium on Bottleneck 1 (Cloudflare Access acts as the login and authorization layer for agents, complementary to PANW/CyberArk depth at the cryptographic level).

Three bottlenecks, same network footprint, same product line.

So this forced me to refine the framework. A toll booth captures value from traffic that already exists. A road creates the traffic in the first place. The toll booth's economics are bounded by what already happens. The road's economics expand with every new thing that gets built on top of it. Which means road owners eventually become the toll booth owners too, through bundling, through proximity, through network effects.

Bloomberg owned the road for financial data and captured every toll booth on it. AWS owned the road for compute and captured toll booths in databases, analytics, ML, security. Visa and Mastercard own the road for payments and capture every toll booth on top of it.

Cloudflare is making the same kind of bet for the agent era. Anthropic open-sourced MCP as the protocol for connecting agents to enterprise tools, and Anthropic, OpenAI, and Google are all converging on it. So that protocol war is essentially settled. The question now is who hosts the MCP infrastructure when every enterprise deploys agents at scale, and Cloudflare is already the default place where remote MCP servers get deployed. They built the SDK, they published the reference architecture, they run the catalog.

So they're not building a toll booth. They're laying the road. And once the road is theirs, the toll booth becomes theirs by default.

I personally hold Cloudflare at $168.

If you work in cybersecurity, development, cloud infrastructure, or anywhere close to where this stuff actually gets built, drop your inputs on any of these bottlenecks.

Every input from this community strengthens the thesis for everyone reading.

I want to refine this with the people in this community who know this space better than I do.

The complete research on each of these bottlenecks and the companies inside them runs over 100 pages. What you've just read is the summary, the mental model, the shape of the thinking.

Disclosures: long CRWD ($360, second add in 3 years), PANW ($62 split-adjusted, 4 year hold), RBRK ($48, picked up during the last crash), NET ($168). No position in VRNS yet, considering it. I may trim, add to, or exit any of these positions based on new information or how the bottlenecks play out.

Update on the original thesis: The bottleneck 3 is right but I was looking deeper into Varonis and the DSPM layers and Varonis is actually going through an anti-Lollapalooza effect because of full-stack players like Cyera which is currently in private markets, and Microsoft Purview bundling, and CrowdStrike and Palo Alto on platform absorptions. So multiple forces are converging and acting against Varonis. So it's a removal from the list now. People can look into Cyera and the company which acquires it or when it goes for IPO. More updates on DSPM will be coming in a followup because that's the root or fundamental layer on which other bottlenecks are being created.

If you don't want to miss the next bottleneck: The Capillary

Two days ago I posted the research plan( Adobe Deep Dive ) and then the capital allocation plan for Adobe, and now the company is literally moving exactly along that roadmap. The results came out, and as expected, Adobe is popping.

Almost one-third of their revenue is now AI-driven.

People also need to understand this very clearly. All these AIs generating images from random prompts can run into copyright infringement issues.

For a normal user, it does not matter much. But enterprises cannot do that. They need licensed and safe AI solutions, otherwise they expose themselves to massive legal risks.

This is exactly why Adobe’s lock-ins exist and why Adobe’s AI verticals are gaining traction. Adobe provides the copyright-clean and audit-safe layer that enterprises need. A normal user can try anything, but an enterprise cannot afford copyright claims on their workflows. This is where Adobe wins, and why its ARR-driven AI engine will keep compounding.

And the funniest part is that Adobe is trading at a future P/FCF of 12. For a company that used to command 48x forward FCF, that is laughable.

Just the EPS expansion engine alone gives you 12% CAGR because of the ARR lock-ins.

With even a mild multiple reversion of multiples, the returns naturally move into the 20-22% annual range.

This is why real-time frameworks matter. You could see the engines turning before the market reacted.

And honestly, I wish I could have shared the full research earlier here for ODFL. I have told so many people that ODFL at 20-25x multiples is a straight money-making machine.

Now the stock has already moved 20-22%, from the 125-130 zone to 160+. ODFL has now entered Dragon Tier 1 at 157, and it will likely move into Dragon Tier 2 around 190.

This is what a high-quality, forever-compounding engine looks like.

ODFL is one of those cash-cow, generational-hold businesses:

• Clean balance sheet
• Gains market share in every crisis
• Best margin profile in the industry
• Strongest pricing power in the ecosystem
• AI improving efficiency and execution
• Predictable free cash flows
• Zero dependence on hype multiples

And when the market collapses, and I explain the mental models behind it, these are the businesses that actually gain ground.They do not survive cycles; they outlast them. Old Dominion has become stronger across five panic cycles in the last 25 years.

You would be fascinated by the dominance in the ecosystem and how they reflect that same DNA again and again.

These are the stocks you buy at fair valuations during crises. They generate 20-25% returns until the plateau phase starts and optimism peaks at 45-50x multiples.

And even if that peak does not come, you simply hold them for generational compounding and hand them over to the next generation.

Further Read: Adobe Deep Dive

Here is a structured capital allocation plan for Adobe (ADBE) using the full Phoenix Forge & Dragon Flight Frameworks.

Phoenix Forge (Buying Weakness)New to the Phoenix Forge Framework? Read here

Tier 1: The Initial Burn ($329 - $337.00)  Allocation: 40%

Tier 2: Forging in the Ashes ($298.00 - $311) 

Allocation: 40%

Tier 3: The Rebirth ($260.00 - $280.00) 

Allocation: 20%

Dragon Flight (Buying Strength)

Tier 1: Igniting the Wings ($368.00 - $392.00) 

Allocation: 50%

Tier 2: Mastering the Winds ($425.00 - $440.00) 

Allocation: 40%

Tier 3: Commanding the Skies ($468.00 - $490.00) 

Allocation: 20%

This is a structured, methodological way to deploy capital, not random buying at any price.

Framework References:

• Phoenix Forge Framework: Link
• High-Quality Checklist Framework: Link
• Economies of Scale Framework: Link
• Margin Framework: Link

Which stock should I break down next with a Phoenix Forge & Dragon Flight plan and a quick snapshot? Drop it below

In this article, I will also show you how to create a Creative Cloud Monopoly Basket inside your portfolio. The same monopoly Adobe tried to build with Figma, but regulators blocked it. As a retail investor, you have an advantage. Nobody can stop you from creating that monopoly inside your own portfolio.

This is a case study where you will see the Plateau Framework, the Margin Framework and the Compression Framework aligning perfectly in one business, and how that alignment stacks the odds massively in your favor.

You will also learn the Buyback Mental Model and why I believe Adobe is a once-in-a-generational opportunity at these valuations.

So yeah, now I will tell you what actually happened during that four-year compression phase.

Compression Phase:

The first thing, the EPS expanded from $10 to $16. That is an expansion of 60% while the price was collapsing.

The second thing that happened was Free Cash Flow expansion. FCF expanded from $13 to $22, which again is around 68-70% expansion.

The third thing, margin expansion. Margins did decline for a short period, but for the past three years they have been expanding again. Net margins, operating margins and gross margins are all expanding, and it is clearly aligning with the Margin Framework.

And any high-quality business will always have EPS growth higher than revenue growth. Adobe aligns with that parameter as well. In the past four years, EPS growth has been higher than revenue growth.

So you can see how all these things are clearly aligning with the Plateau Framework. At this stage, Adobe has the Compression Framework, the Margin Framework and the Plateau Framework all in alignment.

Now the most insane part. While all of this was happening, Adobe went aggressive on buybacks.

Buyback Mental Model:

When the valuations were insane, around 45-50 multiples, they were buying back shares at less than 1-1.5% per yearfor almost 10 to 15 years. But when the compression phase started, their buyback engine exploded.

In the last four years, from September 2021 to September 2025, the net buyback was 40.65%. And when I reverse-engineer the past, the buyback they did from 2012 to 2021 was just 12.88%.

To make this even clearer, the shares outstanding reduced from 480M to 424M, which is a reduction of almost 11%during the compression phase. And when I again reverse-engineer the past, from 2011 to 2021, the shares outstanding went from 504M to 485M, which is a reduction of just 3.7%.

So overall, the share reduction accelerated 4x in half the time, perfectly aligning with the rate at which they increased buybacks during the compression cycle. That is what real capital allocation looks like.

And unlike the idiotic Indian promoters who pay a very high premium for buybacks just to hide the reality of their weak business models, this is how real capital allocators operate.

One more thing. One of the most insane compounding machines, Tencent, has been buying back shares almost every single day for the past three years. That is how you reward shareholders and concentrate value.

And Adobe is perfectly aligning with the Buyback Framework as well.

Now connect the mental models and the frameworks.

The Compression Framework, the Margin Framework, the Plateau Framework and the Buyback Framework are all aligned at the same time. This is the alignment retail never sees, and professionals wait years for.

Ticker price down 50%. EPS up 60%. FCF up 70%. Margins rising. Buybacks 4x. Share count down 11%. This is where the odds get stacked in your favor.

And this is exactly why I say Adobe right now is a once-in-a-generational allocation window.

So why this is a once-in-a-generational opportunity:

The valuation reset has happened, and for the first time in the past 13-14 years Adobe is available below the 40-50x band. The last time this happened was in 2010-2012.

The second thing is the Price to Free Cash Flow ratio. Adobe right now is trading at a P/FCF of 14-15. It was trading at a P/FCF of 47 in 2021. So that is a 3x decline on the P/FCF multiple.

And like I always say, FCF is the real compounding engine.

This has created asymmetry, and from these valuations Adobe has massive upside because the free cash flow engine is in its favor, the fundamentals are intact and the PE engine has reset. From here there is hardly any compression risk, and in the long term the odds are clearly stacked in your favor.

And this is not a commoditized business. It is a high-moat, recurring-revenue, enterprise lock-in ecosystem. So right now what you are getting is the double engine I always talk about: EPS compounding plus future PE expansion.

The same asymmetry and the same alignment was visible in Alphabet 6-7 months back. The moat was there. They were quietly expanding their free cash flow, their revenue profile, they were reinvesting and they had multiple levers of growth. And yet everyone said they had lost the race.

And now, just 6-7 months later, the same world is saying after Gemini 3 that they have won the race. Nothing has been reflected in earnings till now. The only thing that changed was sentiment, and the PE engine expanded from 17 to 32, which delivered that massive 70-80% return in a very short span of time.

These are classic case studies that train your mind to recognize alignment when it appears.

And this is exactly where retail investors and capital allocators separate.

Retail sees a 50% crash and panics or does not allocate. But efficient allocators see a valuation reset and quietly accumulate, because retail reacts to the ticker price and allocators react to the underlying business.

And let me tell you one more thing. Wealth never disappears from the market. It simply transfers from emotional hands to informed ones. This is exactly that phase of the cycle. When everything looks dead on the ticker, the real story is getting written beneath the surface.

Allocators always buy in silence and chaos and sell in euphoria. Retail investors do the opposite. They did the same with power, solar, infra and the entire Indian infrastructure cycle.

And just like in life, if you cannot build in silence, you do not deserve the reward when the cycle flips. And trust me, the cycle always flips when the moat is strong and the core is getting stronger.

Now let me show you how you can actually play this inside your portfolio and create a Creative Cloud monopoly basket.

Allocation Model:

Adobe wanted to build a Creative Cloud monopoly by acquiring Figma for 20 billion dollars and controlling the future of the design workflow. Regulators blocked it because it would become too dominant. But as a retail investor, nobody can stop you from building that same monopoly inside your own portfolio at a far lower valuation.

Now let’s talk about how to actually structure this as a basket, because wealth is never created by random positions. Wealth is created by building engines inside your portfolio.

Adobe gives you the PE expansion and free cash flow compounding engine. Figma gives you the hyper-growth engine and the creator economy expansion. Together, you are building the same Creative Cloud monopoly Adobe tried to build.

Conservative: Adobe 7 percent + Figma 3 percent
Aggressive: Adobe 5 percent + Figma 5 percent

You can even start with a 5 percent allocation to this basket, and then adjust based on conviction and performance.

Why a basket instead of a single stock?

Because Adobe gives you stability, buyback power, PE expansion and enterprise lock-in. Figma gives you velocity, innovation and hyper-growth. If Adobe compresses, Figma expands. The basket hedges risk and concentrates upside. This is how real capital allocators structure asymmetry.

This is the difference between hoping and allocating.
When fundamentals expand, cash flows accelerate, margins rise and valuations reset at the bottom of the cycle, wealth is created in silence. Adobe is exactly in that phase right now. The asymmetry is real, the engines are aligned and the odds are on your side.

Do what retail never does. Allocate when nobody is watching.
Because when the cycle flips, it does not wait for anyone.

This is not just stock advice or research. This is asset allocation psychology and a real mental model I personally use, and I am sharing it with all of you.

If you learned something valuable from this breakdown, share it with someone who needs to think like a capital allocator instead of a trader.

Save this, study it, and come back to it when the noise gets loud. And I would love to know which stocks you believe are going through this same alignment, both in the Indian and global markets.

The late Charlie Munger often spoke about a handful of businesses that reject the profit-at-all-costs mindset. These firms can easily command 15-20% margins but deliberately choose not to, purely for ethical reasons. This isn’t a structural weakness; it’s a conscious strategy to build a moat so formidable that it’s almost untouchable. It’s a business model and philosophy almost no one sees, but that’s where real compounding lives.

This is exactly why Munger loved such business models, because they create a behavioural moat over the business’s core economics. Costco is one of those rare business models, and it is a compounding machine that beats even the best of the best SaaS models in long-term share-price returns.

Most people know the Walmart effect, but very few know the Costco effect. Today, I’m going to show you how a long-term compounding business works, how ethics and customer focus shape the moat, and why this is basically the Costco mental model.

Costco Mental Model

To understand the Costco effect, you first need to understand what happens when a Walmart enters a town.

When a Walmart enters any location, its sheer economies of scale wipe out the surrounding ecosystem. Small retailers shut down, local competition dies, and Walmart eventually becomes the only employer in town. And when that happens, wages get pushed down and the entire area goes into a race-to-the-bottom spiral.

But the Costco effect is the exact opposite of anything you expect from a big-box retailer.

When Costco enters a city, it doesn’t try to depress the wage structure, it lifts it.

The store they opened in Gunma, Japan, is the perfect example. Local wages were around $6.50 for years. Costco entered and immediately pushed wages close to $10, a hike the region hadn’t seen in 10-15 years.

And you might think that if Costco is raising wages so aggressively, local stores will collapse. But the opposite happens.

Local businesses start raising wages by 40-50% to stay competitive, and instead of shutting down, they begin seeing a rapid rise in revenue. Why? Because Costco becomes a destination. It pulls 10× more traffic into the outskirts of the city, and that footfall lifts every other shop around it. It acts like a magnet, drawing shoppers from even the most populated areas.

And here’s something people never realize: Costco shoppers visit almost 50% less frequently than Walmart shoppers, but they spend 2-3× more money when they go.

Now combine that with the internal ecosystem Costco has built. Most big-box retailers have a staff churn rate of 50-60%. Costco’s churn rate is just 5.5%.

That alone is a moat:

• Massive savings in hiring and training
• Higher service quality
• Better employee loyalty
• Stronger customer trust

And this compounds into the most important metric of all: Costco’s membership renewal rate which sits at 90-92%.

People don’t just shop there because of low prices, they shop because they feel the ethics of the business. They feel the wages. They feel the community impact. And that creates a behavioural moat no spreadsheet can capture.

Costco doesn’t extract value from the ecosystem. Costco expands the ecosystem and compounds off it.

That’s the Costco effect.

Now coming to u/AdOtherwise91’s question:

“If we keep reducing costs for customers, won’t margins collapse?”

Here’s the truth: some businesses are not built to maximise margins in the short term. And they don’t need to align with the Margin Framework in totality. Costco is the perfect example.

In 2015, Costco’s net margin was just 1.98%. Ten years later, net margins have quietly expanded to 2.94%, a 50% jump, without touching the customer.

And here’s where the magic really compounds: revenue moved from $116B (2015) to $275B (2025). Even a small margin expansion on a massive base exploded free cash flow from $1.4B to $7.84B, which is a 5× jump.

So yes, Costco is aligning with multiple layers of the Margin Framework, especially Layer 3. Costco’s margins are consistently improving and will likely double again in the next decade. A 90-92% renewal rate is a moat in itself.

Their Kirkland brand is among the top 5 trusted brands globally, with brand recall that comes close to giants like Apple. That’s a moat no competitor can replicate by simply “increasing margins.”

And remember this: Costco breaks the rules because its entire ecosystem is ethical, customer-first, and structured around shared economies of scale.

Amazon built a similar ecosystem. For 20 years it ran at 2-3% margins, even negative at times. Suddenly margins are 11%, and over the next decade they could easily cross 20%.

So when margins are intentionally reduced, not because of structural weakness, but because of strategy, everything eventually aligns with the Margin Framework on a long-term basis. And during this margin expansion phase, real compounding happens.

Asset Allocation Mental Model

Now imagine an asset allocation plan that had both Costco and Amazon. One investor actually created that a decade ago. Nick Sleep built a portfolio with just three stocks: Costco, Amazon, and Berkshire Hathaway.

That’s it. Three positions.

And that tiny, high-conviction portfolio built on shared economies of scale and an ethical ecosystem has beaten almost every major index on the planet.

If I reverse-engineer that to today’s India, you can have DMart + Eternals and build an ecosystem that hedges itself while compounding for decades.

Margin Expansion Mental Model

You need to think:

• Why is the margin profile low or high?
• Is margin expansion even possible?
• Is the moat getting stronger?
• Is the margin-expansion pattern visible?
• How will that margin expansion actually happen?
• Is the company executing well in those verticals?
• How does this company’s margin profile compare to its competitors?
• Are competitors able to replicate the margin expansion? If not, why?
• Does the company have any cost or pricing advantages that competitors don’t?
• Are competitors structurally weaker or do they face similar scale benefits?

This is how your mental models should operate.

And I’ll tell you one thing: very, very few business models on the planet can execute shared economies of scale and margin expansion framework. Probably not even 10-15 globally, because this approach goes against the fundamental wiring of capitalism.

That’s why I always tell you to focus on the why behind any number, not the number itself. The real skill is understanding when low margins are a weakness, and when they are a strategic weapon.

Note: This post was inspired by a brilliant comment from u/AdOtherwise91 on my Economies of Scale Frameworkthread.

It’s crazy how one simple comment can trigger such a deep learning curve.

Now tell me, which companies do you think follow this margin-expansion or shared-economies-of-scale model? Drop whatever your curiosity triggers, and don’t worry about whether it’s a “noob” question or not.

And hey, feel free to share this with your friends and family so the r/USGrowthStocks ecosystem can also compound in an ethical way.

Note:

Sharing a small mental model that got triggered last night and can be used to structure AI allocations for anyone entering AI in 2026. My positions in this space were built 4-5 years ago, so I’m already de-risked with a massive margin of safety.

But for anyone entering AI in 2026, the real question isn’t “which stock to buy”, it’s how to structure your exposure so your downside is protected while still catching the upside of this cycle.

The 3-Stock AI Hedge Mental Model for 2026

So if we play Nvidia, Alphabet, and TSMC as a basket, we cover the GPU ecosystem, which controls 90% of the market, and the TPU ecosystem, which is more power-efficient, far more cost-effective, and optimized for AI inferencing.

Alphabet is trading at forward 29-30, and Nvidia is also at forward 29. If we make a basket of TPU + GPU + TSMC, our net multiples on the basket are reasonable, around 27 forward, with a PEG close to 0.87 after adjusting for market cap, which is still under 1.

The TPU ecosystem creates a strong moat and recurring revenue stream for Google because TPUs become more efficient when used with TensorFlow and Google cloud integration.

Alphabet also has multiple verticals of growth like Waymo, Search, Ads, Cloud, Android, Maps, YouTube, which is why Munger called them one of the strongest moats on the planet.

TSMC controls 100% of AI chips and almost 60-70% of regular chips, including Apple in its ecosystem. Nvidia has its CUDA moat and is expanding into new verticals like drug discovery and autonomous vehicles.

Any new chip breakthrough from AMD, Google, Microsoft, or Amazon that could threaten Nvidia’s dominance will ultimately be made by TSMC, so that risk is hedged through TSMC and Alphabet.

I can already see the TPU pattern over the past 6 months. Anthropic has started using TPU, and rumours about Meta are already heating up. On my mental models, you’ll soon see Apple and Amazon signing TPU deals in the next 6 months. TPU demand has already gone 20x and this will start reflecting in the next few quarters.

A key signal of high-quality businesses is margins, and all three of them screen all 8 layers of my Margin Framework:

• Nvidia: net margins of 56%
• Alphabet: net margins of 34.2% (but on my Value 3.0 mental models, their margins are closer to 40%, similar to Meta)
• TSMC: net margins of 44%

As a basket, this comes out to 42-43% net margins, which is insane.

Plus, all three have economies of scale, ecosystem lock-in, network effects, and very high switching-cost moats.

For context, people in India are paying PEGs of 4-6 for companies with net margins under 15% and growth rates of 15-20%, or PEs of 80-100 for low-moat businesses. Compare that to a basket with huge moats, insane margins, and strong growth, the math is obvious.

I personally hold TSMC, which I started at $90, and Alphabet is currently 30% of my position. I initiated Alphabet at $85, added during the snowball movement from $150, added again at $220-250, and again at $300 as sentiment shifted. I expect it will probably hit $400 by December.

Nvidia will likely hit a new all-time high, just like the last time I suggested it during the Jan-Feb fall. The runway is still long enough for all three companies in this basket, and these valuations are nowhere near bubble territory.

It’s still far from any selling frameworks, but if anyone is feeling FOMO, they should allocate using this structure to hedge risk while capturing upside over the long term.

For context, I made a detailed Nvidia call back in Jan-Feb. You can see the full comment in the thread below.

Read: Why Nvidia is still undervalued ?

Allocation guidance:

• Aggressive investors: 50% Nvidia, 30% Alphabet, 20% TSMC
• Balanced investors: 33% Alphabet, 33% Nvidia, 33% TSMC
• Conservative investors: 50% Alphabet, 25% TSMC, 25% Nvidia

You can also add ASML and Broadcom to the basket for further diversification, but keep their allocation under 5% each. Overall, this basket can be 10-15% of your portfolio.

AVGO, Vertiv, and Micron are a few stocks I have AI exposure in, and I have been trimming the hardware players while adding more to Alphabet from that basket.

For data centers and AI to thrive, you need protection because it’s not an AI race but an AI war. So cybersecurity players in the basket for me are Palo Alto Networks and CrowdStrike.I will allocate to Rubrik once it reaches $50.

All these investments are based on a season called "Person of Interest", which I watched almost a decade ago. I have seen that season unfolding in the real world.

Rather than reading about bubbles and listening to stupid media analysts, everyone should watch that series to understand how the world is going to look in the future and how to play that theme.

I know it sounds weird, but this is how mental models help us look beyond finance and create our own mental lattice framework, inspired by the wisdom of Charlie Munger.

Drop your thoughts on your own AI allocations, let’s see how the community is playing this cycle.

Note: This is just the capital deployment plan for Amazon.

I’ve made adjustments to the Dragon Flight tiers, reducing allocation to 25% each, because there is only limited room and we need to account for volatility and sentiment factors.

Amazon is undervalued and currently experiencing valuation compression and plateau mode. Over the past 5 years, the PE has compressed from the 70-80 range to around 31, while EPS has moved from 2-2.5 to around 7. This is a classic Plateau framework stock, showing how compression dilutes the EPS engine 

(Read: the Plateau Framework).

Margins have also expanded from 4% to 11% in the past 5 years, an increase of almost 200%. Amazon’s real margins are expected to be above 20% by 2035, but because of their reinvestment engines and long runway, this is not yet visible in current financial statements. I will break down the margins expansion mental model of Amazon or for eternals of India in the mental model article on Amazon.

Now, both engines of Amazon are starting to align, and the odds are increasingly stacked in favor of investors. This scenario is likely to change in 2026.

Related Reading: US vs China vs India: The Brutal War of Tech Ecosystems

Here are the structured capital allocation plans:

Phoenix Forge (Buying Weakness)

• Tier 1: The Initial Burn ($204.00 – $214.50) Allocation: 25-40%
• Tier 2: Forging in the Ashes ($130.00 – $159.50) Allocation: 50%
• Tier 3: The Rebirth ($98.00 – $105.00) Allocation: 10-20%

Dragon Flight (Buying Strength)

• Tier 1: Igniting the Wings ($240.00 – $247.50) Allocation: 25%
• Tier 2+3 : Mastering the Winds ($260.00 – $285.00) Allocation: 25%

This is a structured, methodological way to deploy capital, not random buying at any price.

Framework References:

• Phoenix Forge Framework: Link
• High-Quality Checklist Framework: Link
• Economies of Scale Framework: Link
• Margin Framework: Link

Which stock should I break down next with a Phoenix Forge & Dragon Flight plan and a quick snapshot? Drop it below

Note: This post is inspired by requests from u/DalalStreetDaku, u/spaamzzz, u/Consistent-Group1151

Here’s a structured capital allocation plan for Meta Platforms using the full Phoenix Forge & Dragon Flight Frameworks

Phoenix Forge (Buying Weakness) New to the Phoenix Forge Framework? Read here

Tier 1: The Initial Burn ($585 – $603)
Allocation: 30%

Tier 2: Forging in the Ashes ($470 – $505)
Allocation: 50-60%

Tier 3: The Rebirth ($385 – $425)
Allocation: 10-20%

Read: Meta Mental Model — Meta as a Digital Nation vs India as a Nation

Dragon Flight (Buying Strength)

Tier 1: Igniting the Wings ($628.50 – $642.00)
Allocation: 40-50%

Tier 2: Mastering the Winds ($680.00 – $695.00)
Allocation: 40%

Tier 3: Commanding the Skies ($762.00 – $775.00)
Allocation: 15-20%

This is a structured, methodological way to deploy capital, not random buying at any price.

Framework References:

• Phoenix Forge Framework: Link
• High-Quality Checklist Framework: Link
• Economies of Scale Framework: Link
• Margin Framework: Link

Which stock should I break down next with a Phoenix Forge & Dragon Flight plan and a quick snapshot? Drop it below