Oracle: The Time Has Come

Oracle's cloud pivot is now more derisked than it's ever been.

“Founder mode”—this is the term coined by Paul Graham, the founder of Y Combinator, the most prestigious startup accelerator in the world.

Founder mode happens when an entrepreneur acts like he is still the business, because this is how it is when companies are young. There is nothing as “business” on the first day; it’s a founder and an idea that would hopefully make people pay.

Oracle Founders in early days: Ed Oates, Bruce Scott, Bob Miner, and Larry Ellison (right) celebrate Oracle’s first anniversary

The founder builds the product, the founder does the marketing and sales, the founder keeps the books, solves problems, and thinks of ways to make the product better.

As the company grows, the founder often assumes a more managerial position. Now he says what needs to be done, and it’s up to his direct reports how to do it.

Founder mode is on when a founder refuses to be confined and stays in the game.

When founder mode is on, impossible becomes at least feasible.

Because all the great success stories start from the impossible. Only 0.0001% of all startups become unicorns. All of them were built by some crazy people working in founder mode. Founders beat the odds.

Some of them do it over and over again. Take Steve Jobs.

Found Apple, fired from Apple, built Next, built Pixar, returned to Apple, built it again to the most valuable company in the world. He beat the odds every time.

Why did I say all these? Because if it wasn’t for the founder mode, even the thought of what Oracle is trying to do would be insane. It’s literally been impossible so far.

No business making above $10 billion in revenue has ever grown 75% annually for 5 years. This is exactly what Oracle is aiming for in its cloud infrastructure business:

It dared to undertake this; it dared to risk it all to pull this off, and it dared to let all of us know this because its founder, Larry Ellison, is still at the helm as CTO and Chairman, and his founder mode is turned on.

It doesn’t take much brains to see that it’ll be an immensely more valuable company if it can pull this off. The market struggles to believe it.

Can he pull this one off? This is what we’ll dive deep into today.

But.. Let me say this for now—before Larry Ellison built Oracle, there was no relational database company called Oracle. With founders, it’s always from 0 to 1.

Let’s get into it.

What are you going to read:

1. 🏭 Understanding The Business

2. 🏰 Competitive Analysis

3. 📝 Investment Thesis & Risks

4. 📊 Fundamental Analysis

5. 📈 Valuation

6. 🏁 Conclusion

---

🏭 Understanding The Business

Great companies that last for decades can do so because they are often rebuilt multiple times, catching the next wave each time.

And nobody is better at rebuilding companies than their founders…

Steve Jobs built Apple twice.

He built it around personal computers in 1976, and rebuilt it around consumer electronics in 2001 with the release of the iPod. Apple is great because it has withstood the ravages of time and adapted to changes that have destroyed many.

If he were alive, he would surely be rebuilding Apple for the next wave. But most of the great entrepreneurs born in the 1940s—1950s and rode the post-war investment boom, creating great companies in the 1970s—1980s, are either dead today, like Jobs himself, or have long been on the sidelines like Bill Gates.

But one man is standing, Larry Ellison, and he is building Oracle for the third time.

The first one was in 1977. It was 10 years after Larry Ellison drove to California after dropping out of two colleges. He was academically gifted but temperamentally allergic to authority. He couldn’t do well in a top-down academic hierarchy.

When he drove to California in 1966, he had no degree, no plan, and no money, but had a working knowledge of computer programming picked up during his brief university stints. For the next decade, he jumped from one technical work in the Valley to another.

In 1977, he was working at Ampex, where he helped build a database for the CIA in a project code-named “Oracle.” In mid-1977, Ellison and two colleagues from Ampex decided to create a company. They called it Software Development Laboratories (SDL). It was a generic name because they didn’t know what to do; they didn’t have a product.

Then Ellison came across a 1970 paper by Edgar Codd, a British computer scientist working at IBM's research labs in San Jose. It was titled “A Relational Model of Data for Large Shared Data Banks.”

What he was proposing was mind-numbingly simple, yet novel.

Data was traditionally stored in flat files, where records were organized by category in pure text format. This was basically the digital version of the file cabinets. Data manipulation and retrieval required custom coding.

What Codd was proposing was storing data in relational tables, where each data point is written in a row and gets a unique ID, while columns correspond to attributes. These IDs create relations when used in different tables.

Imagine you have three tables. One defines the products, where each product gets an ID corresponding to its attributes; one defines customers; and one records transactions. In the transactions table, you no longer need to show all the attributes of products and customers who bought them. You just use the IDs, and you have it all:

IBM sat on this. Perhaps they didn’t even notice the importance of the paper, as their research output was pretty large at the time, the same way Google was late to capitalize on their own AI research around transformers.

But Ellison understood it at the moment, and he thought this would be the default way of storing data. IBM had also published the specifications, so there was nothing stopping a small firm from building a commercial product.

His other co-founders, Bob Miner and Bruce Scott, started writing the code, since they were technically stronger, and Ellison began selling even before the product was finalized.

He remembered that the CIA wanted a database, and he himself worked in the Oracle project at Ampex. He leveraged connections he made there and convinced the CIA to give them a contract for a relational database. They made the product and named it “Oracle.”

They went beyond the CIA and commercialized it in 1979. The demand was insane. They changed the company name to Oracle in 1982 to match the product everybody knew, and went public in 1986 at a $270 million valuation.

This was the first time Ellison built Oracle on a relational database.

In the early 2000s, he had to rebuild the business. It was forced by the technological changes underway. The next wave.

At the time, the enterprise software was having its boom. Computers for each officer had become standard in most businesses across the developed countries, so everything was running on enterprise software. Resource planning, customer relations management, bookkeeping, etc., everything.

It was actually a good thing for Oracle because these applications needed databases underneath, and most enterprises were using Oracle as the database.

But there were two problems:

• Database business was maturing while software was growing skyrockets.

• Growing footprint of enterprise software players was a business risk for Oracle.

If those software players fine-tuned their software to work best with another preferred database, they could even build this themselves; Oracle would lose business.

So, vertical integration by expanding to enterprise software was a strategic mandate for Oracle, not an option. If it pulled it off, it wouldn’t just secure its legacy business but also create a new source of substantial revenue.

For Oracle, the limit wasn’t money but time. It had giant cash flows from its database business, but it needed to act fast. Thus, instead of building, they acquired, embarking on one of the most ambitious roll-up sprees in tech history, one that everybody thought it would fail.

It didn’t fail. It worked out phenomenally.

Oracle acquired over 50 companies between 2005-2010, most of which were enterprise software like PeopleSoft (HR & ERP), Seibel Systems (CRM), Hyperion (finance), etc.

As a result, Oracle transformed into a gigantic vertically integrated provider of software and infrastructure:

This was the new Oracle, maybe version 1.5, but not 2.0.

It was vertically integrated, spanning application and database layers, as young in 2010 as it was in 1999. Yet, it had to change again, before it squeezed the juice out of its new structure. This time, it was an absolute necessity.

In 2006, Amazon changed how digital companies were built and run by launching a public cloud service within Amazon Web Services (AWS).

Before AWS, every startup needed to buy its own servers and host their applications and databases within them. This came with a significant hardware overhead. Public cloud service allowed them to rent servers online and host their databases and applications in those servers.

This changed the game not just for newcomers, but also for the incumbents like Oracle, as it brought a completely new distribution model.

Before the cloud, software generally worked on a perpetual licensing model bundled with regular maintenance services. With the cloud, companies could now offer their software on a subscription basis, and customers would get the new versions automatically.

That changed the game. Customers loved it, and cloud native software competitors like Salesforce and Workday started to eat away at Oracle’s market share.

That wasn’t the only threat Oracle was facing. Its legacy database business was also under threat because all the new big companies like Airbnb and Uber were built on the cloud, so they were using native database services that their cloud players were offering.

If Oracle couldn’t adapt, it would have 0 exposure to new giants and would slowly die.

They built Oracle Cloud in 2014, which got a complete overhaul in 2016. The new version was tightly integrated with the Oracle database, offering the ability to rent physical servers (bare metal), not just virtualized containers, and it was priced lower than AWS. In 2019, Oracle also implemented the multi-cloud strategy, which is basically the distribution of the Oracle database through other cloud providers as well.

By 2020, Oracle had largely adapted to the cloud. Its software was being distributed both through traditional licensing and its own cloud, while its database was distributed through traditional licensing and its own cloud, and Microsoft’s Azure.

This was Oracle 2.0—the survived and thrived through two decades of transformation, first adapting to software and then integrating cloud.

Adapting to the cloud didn’t just secure Oracle, it also put it in a position to capitalize on possibly the biggest technological revolution since the internet—AI.

Enter Oracle 3.0.

Oracle was a latecomer to the cloud. That generally means disadvantage, but sometimes, it’s an advantage, especially when something happens, and incumbents will need to adjust to capitalize on that. In that case, latecomers, with their newer stack, can act faster and capitalize on the opportunity.

This is exactly what’s happening with Oracle Cloud Infrastructure (OCI).

To his discredit, Larry Ellison was late in seeing the transformative effect of cloud computing. In 2008, he said:

“The interesting thing about cloud computing is that we've redefined cloud computing to include everything that we already do... I don't understand what we would do differently in the light of cloud computing, other than change the wording of some of our ads.” — Larry Ellison, 2008

This is why Oracle was late to the cloud; it launched OCI 10 years after AWS.

But one of the common properties of great men is that they draw lessons from their mistakes and use them to improve their vision. Ellison had drawn his lessons from underplaying the cloud, so he didn’t repeat it when ChatGPT was launched in 2022.

He immediately envisioned that LLMs would be incorporated into almost every technology, and we would need a lot of compute power to run them. We would need:

• Enormous GPU clusters

• Ultra-fast networking

• Massive power and cooling

That kind of computing required specially designed data centers and cloud infrastructure to allow customers to manage it.

He knew that the legacy players like AWS, Azure and Google Cloud were built for general-purpose computing. Also, these companies were now run by professional managers, whereas Ellison still directly controlled Oracle and owned over 40% of shares. So, he knew he could act and deploy the capital faster than them.

This was his opening to catch the next edge.

Oracle started aggressively deploying capital and doubling the capex in 2023, which led to its first OpenAI deal. This signalled to the whole industry that Oracle was capable. Over the next two years, it expanded its partnership with OpenAI and added new customers like XAI, Cohere, and META.

Result? Capex skyrocketed, of course. It now expects to spend over $400 billion through 2029. Cloud revenue expectations, naturally, surged as well:

For reference, Oracle’s whole revenue in 2023 was $50 billion. This is probably the most ambitious transformation project a company of the size of Oracle has ever embarked on.

This is Oracle 3.0, the third time the business is being built by its founder. At the age of 81, his founder mode is still turned on. This is why this article opened with reference to founder mode. Do you think the management would bet the whole company on this if it weren’t for the vision and pushing of Ellison? I don’t think so.

Step by step, he turned a database company into an ecosystem of complementary tech businesses that are also complementary to each other—database, enterprise software, and cloud infrastructure.

Can he pull off Oracle 3.0 as well and make Oracle into one of the largest AI infrastructure businesses in the world? It’s a very narrow corridor.

Oracle’s ability to pull this off depends on being able to actually spend and build the infrastructure, which depends on them keep generating cash flows. But these cash flows, especially the part that comes from software, are under threat as AI is eroding software moats. Then it needs to outmaneuver the competition, other cloud providers.

Can it? It’s hard, but there is a path. Let’s dig.

---

🏰 Competitive Analysis

Chris Hohn and his hedge fund TCI broke the hedge fund earnings record last year. He is one of my favorite investors, and once asked about his strategy, he said “very few things matter, 90% of the things are noise.” When the interviewer asked what matters, he said, “competition matters.”

He then opened it up: “Competitive advantage has two elements—resistance against disruption and resistance against competitors.”

Indeed, if the company has these two, it’s very hard to lose money on it, provided that you don’t overpay for the stock initially. If it has ample growth opportunities on top of this, you can make significant money over time.

So, we need to evaluate Oracle’s business ecosystem from this perspective. Let’s look at the snapshot first:

The majority of Oracle’s revenue comes from enterprise software applications and database franchises. Services and hardware are byproducts of these businesses, and they aren’t critical to the broader business at this stage. Software and application cash flows fund the cloud infrastructure unit, which is the current growth engine.

Database segment is perhaps the most straightforward one and doesn’t require much deliberation. Oracle is the most popular relational database in the world, and it controls 30% of the market according to DB-Engines Rankings.

Databases are deeply embedded in enterprise workflows, and they are very sticky. There are very few things a company can gain by changing its database provider, and it comes with a nasty process. Migration could spawn many problems, and data loss during migration constitutes a strategic risk.

As almost all businesses will need some type of database regardless of how AI transforms the application layer, we can say database cash flows are safe, and we could assume it to grow at least as fast as the market, given Oracle’s dominant position. Growth will be around 10% annually by 2032, according to Market Research Intellect, and it’ll be driven by surging enterprise data volumes thanks to AI.

Enterprise software segment has a bit more nuanced situation. The root cause of the complication is the risk of AI making software companies redundant, eating away at their margins first and then completely commoditizing them.

Here, we have to make a distinction between consumer and enterprise software. Yes, AI will commoditize consumer software, and it’s already happening. This is because there is generally no embeddedness and system dependencies in consumer software, so switching barriers almost don’t exist, and customer inertia is low.

Enterprise software, however, tends to be embedded in workflows. Especially the foundational level enterprise software like ERM, the kinds that Oracle sells, have many system-level dependencies, thus switching barriers and customer inertia are way higher.

This prevents each company from developing its own foundational enterprise software, for now. However, it surely undermines the industry’s pricing power. Incumbents can’t price as aggressively as they used to because development costs are plummeting.

So, the basic proposition here is that enterprise software will be safe as long as the cost of development, maintenance, and migration doesn’t drastically drop below what they are already paying. This won’t happen soon because inference is still costly, and you still need to employ top-level engineers to build, deploy, and maintain the systems. Inference expenses alone would be way beyond what many customers are now paying for their software.

Companies are already struggling to stay within their inference budgets:

Thus, though possible, mass replacement of already embedded foundational enterprise software won’t be economically feasible soon. Given high switching barriers stemming from system dependencies, staff education, and inertia, we can also say Oracle’s software stack is protected against competitors. So, cash flows from this segment are also safe for the short and medium term.

Now, let’s turn to cloud infrastructure.

This is the best type of market you could ever find:

• It’s growing very fast thanks to the heavy compute demand of AI.

• It’s an inherently sticky business.

It’s sticky for two reasons.

First, demand is recurring. AI labs always need the capacity to train new models and serve existing models on the inference side. Thus, if your cloud provider is satisfying your needs, there is almost no reason for you to dump it. If you fall short of computing for some reason, you’ll lose market share to competitors.

Second, it comes with workflows and system dependencies. Once you build the business, workflows, and dependencies, switching means operational risk. Even if you are natively multi-cloud, dumping mega-watts, or even giga-watts of instances and reallocating that capacity to another provider is an operational hustle. Why would you do that as long as it satisfies your needs, provided the price is more or less the same?

You wouldn’t. Thus, competition in this market becomes pretty much muted in this market after you land the customer. Much of the competition occurs to land the customer. Given that cloud business is not a mystery and all the primary providers can satisfy the needs of the customers, the decisive factor in customer acquisition is how fast you can bring large volumes of capacity online and offer your customers.

This is where founder mode becomes relevant again.

Ellison is basically betting the company on this business, so they are moving way bolder and faster in responding to demand. As long as there is demand, they are in the pole position with Amazon, Google, and Microsoft triopoly to capture it, and maybe a step ahead because their decision process is faster, as the founder is still at the helm. And once they capture it, competition becomes muted.

In short, we are looking at a business that doesn’t have much trouble with competition.

• The database business is dominant, and it’s not under a threat of disruption.

• Software business is inevitably threatened by AI, but the short- to medium-term is safe.

• On the cloud side, the competition happens for customers and becomes muted after landing them. However, the demand is so strong right now that nobody will feel competition for customers in the medium-term. They’ll sell as fast as they build.

So, we don’t need to worry about competition, and when the competition is out of the way, it all comes down to two things—the market development as expected, and execution.

---

📝 Investment Thesis & Risks

Oracle has always been a good company, but we wouldn’t have been talking about it as an investment opportunity if it weren’t for the cloud business and the immense demand for compute. This is where the whole investment thesis depends on.

Market research firms estimate that the global cloud computing market will reach $5 trillion by 2035:

Thus, the thesis is simple:

AWS targets $600 billion in revenue in 2035. Even if Oracle can reach half of AWS’s size, we are looking at $300 billion in revenue only from OCI, which generated just $18 billion last year.

Up until recently, there were two main risks to this thesis:

The first one is the reliability of the demand. Most of this demand will be driven by AI, the training and inference needs of AI models. If you have been following this publication for a while, you know that I have been pretty skeptical about AI labs’ ability to scale revenue as they forecasted. I no longer think that.

The latest generation of models has reached a set of capabilities where they can now be deployed as full-time workers, instead of being used just as productivity boosting assistants. They are now so good at some tasks, like coding, that the productivity barriers that we used to face no longer exist. The only limit is imagination.

As a result, the demand has exploded. Anthropic’s revenue ramp from $10 billion in January to $30 billion in March proves this:

This is because they are now able to do things that they weren’t able to do before, so people can actually be productive with them now. Not just enterprises, everybody.

And this is just the beginning. We are looking at a general-purpose technology, which means it will help spur further innovations and products. Indeed, almost all electronic devices we are using today can be made smarter by incorporating AI. This will be made gradually, and the demand will jump at every step. They’ll also enable new products that we can’t even imagine today, and they’ll drive the demand even further.

Take the internet as an example. It’s been 23 years since it was commercialized, but internet traffic still grew 19% in 2025. It’ll be the same with AI; we’ll see decades of demand growth through capability expansion and new product discovery. So, I think broader demand risk no longer exists. There’ll be more demand than we can imagine.

The second main risk to the thesis was the concentration of the $553 billion backlog in commitments from OpenAI.

Oracle expects to spend over $400 billion in capex through 2029 to expand its cloud business. Given that its free cash flow before the investment supercycle began was around $11 billion, the vast majority of this buildout will be funded by debt. And its ability to pay this debt depends on OpenAI’s ability to meet its commitments.

OpenAI’s ability to pay, on the other hand, depends on two factors:

• OpenAI’s own revenue ramp.

• OpenAI’s solvency.

Both of these were in question until recently because OpenAI guided for $200-$250 billion in revenue in 2030. Despite this growth, it projected $115 billion cash burn through 2030.

This scenario got largely derisked over the last three months.

First, Anthropic’s growth showed that OpenAI’s projections aren’t bullshit. Demand is there. Second, OpenAI recently raised $122 billion and will likely do an IPO in the next 24 months, and will likely raise at least another $50 billion. The solvency risk no longer exists if OpenAI doesn’t fail miserably.

So, the cloud growth thesis now has the best outlook ever since Oracle kicked off this pivot.

This is why I said “the time has come” in the title. Cloud growth thesis has never been as derisked as it is now. Main external risks have largely vanished, and the faith of the thesis now depends mostly on Oracle’s own execution.

The time has truly come.

---

📊 Fundamental Analysis

➡️ Business Performance

We need to see Oracle’s performance before and after they doubled down on cloud growth in 2023.

Its performance was already solid before 2023 for a software+database company at its size. It was consistent, but the growth was, of course, problematic. Post-2023, we see a meaningful acceleration in growth, thanks to the cloud pivot:

The revenue jump in 2023 was largely driven by the accelerating distribution of enterprise software through Oracle Cloud. Though this was largely a one-time bump as enterprises sought to modernize their digital infrastructure post-COVID to adapt to changing work environments, such as the rise of remote work.

Thus, we saw that the applications cloud services revenue jumped by 32% in 2023, driving the overall growth in the business. Cloud infrastructure revenue grew by only 6% in that year. However, post-2023, cloud application services rapidly went back to single-digit growth while cloud infrastructure accelerated every year:

In short, we are looking at a company that is already strong in its core and aggressively capitalizing on a new opportunity. Cloud is not a distant story for Oracle; it’s become the company’s main growth engine since 2023, and it’s just the beginning. There are no red flags here, only hope.

➡️ Financial Health

This is where most people who consider investing in Oracle turn back. The headline numbers look scary. Oracle has over $108 billion in financial debt while it generates just above $27.7 billion in EBITDA and carries $38.5 billion in equity:

However, when we lean into details, it’s much less scary. Oracle’s principal debt, as of the last 10-K, was as follows:

• Fiscal 2026: $7.3 billion

• Fiscal 2027: $5.7 billion

• Fiscal 2028: $10.1 billion

• Fiscal 2029: $2.0 billion

• Fiscal 2030: $7.3 billion

• Thereafter, all the way to 2065: $60.5 billion

The average coupon rate on this debt was roughly 4.5-4.7%. So, Oracle’s interest payments will look as follows:

• Fiscal 2026: ~$4.0 billion

• Fiscal 2027: ~$3.9 billion

• Fiscal 2028: ~$3.7 billion

• Fiscal 2029: ~$3.3 billion

• Fiscal 2030: ~$3.2 billion

• Thereafter: roughly $55–60 billion

Of course, this doesn’t include ~$15.5 billion in new debt raised since May 2025, but the overall picture is simple. At the current state, Oracle’s total debt service won’t likely exceed, on average, $15 billion annually by 2030. So, if the cloud revenue ramps as expected, assuming 30% EBIT margin, which is similar to AWS’s margin profile, debt repayment won’t be an issue.

In the last earnings call, the management said they don’t need to raise any more money in 2026 to fund the year. Bloomberg expects FY 2027-FY 2029 to create another ~$40-$50 billion funding gap, and free cash flow will be around +$20 billion in 2030.

Even if that gap is bridged solely by raising additional debt, it’ll add only around $2.2-$3 billion in annual interest payments, assuming similar coupon rates to its existing debt, and it won’t need to raise more beyond 2030 as +$20 billion starting FCF is enough whether they want to leverage down by paying the principals as they hit maturity or refinance and roll over.

So, numbers aren’t as risky as people think. The fact that Fitch also affirmed Oracle’s credit note as investment grade (BBB) in February illustrates this. The main risk to its financial planning was never the numbers themselves; it was whether the revenue would ramp as expected.

As I said above, given the current demand outlook and OpenAI’s improved fiscal visibility have largely secured Oracle’s expected revenue ramp going forward.

To sum up, we are looking at a business that is in the middle of arguably the biggest corporate pivot in history. Performance numbers show that the plan has worked so far. The balance sheet is leveraged indeed, but it’s actually less risky than it looks on the surface. Risks were never in their numbers, but in their thesis and dependencies. These risks have substantially abated lately, so their position is also stronger than ever.

---

📈 Valuation

I see many detailed valuation projections for Oracle, and they all suffer from the same defect—the numbers are just too speculative.

Some assume a $110 billion peak capex, and some others assume $65 billion, some assume an EBIT margin around 20%, and some others around 35%, etc. I don’t think it makes sense to create a detailed model with that much speculation. It only makes something too speculative look much less speculative, which is meaningless.

As Aswath Damodaran says, the best valuation is the simplest one with the least number of dependencies. In the end, valuation is a scenario, not an objective work. We create a scenario and value it.

If you have foreseeability in the business and financials are strong enough to guarantee survival, the only thing you need to do is create a conservative scenario and run the math.

What would be a conservative scenario for Oracle?

The management is guiding for $224 billion in revenue in 2030 and expects $166 billion of it to come from OCI. To be conservative, I assume it’ll be 20% less than the management’s target, which gives us $180 billion revenue in 2030.

Microsoft has the most similar business mix to Oracle, with software and cloud. Their net margin has been around 35% for the last decade. I assume Oracle will have a lower net margin due to its faster and recent cloud ramp and will end up with around 25% net margin.

This gives us $45 billion in net income in 2030. Assuming a 20x exit multiple, we’ll have a $900 billion company. Discounting it back to today at 10% annual rate gives us around a $615 billion company.

It’s currently valued at $503 billion, implying 22% discount to our fair value.

---

🏁 Conclusion

Oracle is trying to pull off arguably the biggest corporate pivot of all time. No public company generating above $10 billion in revenue has ever grown as they are trying to do with their cloud business.

I have been very skeptical about their efforts initially, not because of how the financial projections were, but because of the assumptions those projections relied on.

The biggest risk to Oracle's thesis has been the overall demand for AI models and services and OpenAI’s ability to ramp revenue fast enough to meet its obligations to Oracle. These two risks have now substantially abated as Anthropic’s recent revenue ramp proved that the demand is there, and the frontier AI labs can grow revenues incredibly fast if they have the compute.

Valuation is also attractive even if we assume substantially lower targets than those of the management. In short, we are now at a point where the risk/reward of betting on Oracle’s pivot is at its most attractive point since it embarked on this path.

Is it at the “take a large position” point? Of course it’s not. The risk is still serious. Something may happen tomorrow, and the demand projection for AI could change completely. But, it’s attractive enough to take a 3-5% position. After all, if the management’s guidance materializes, the current price implies 8x earnings in 2030 for a hyperscaler business that will still be growing by +20% beyond 2030.

Add all these above the fact that this pivot is driven by its founder, who managed to grow the business through major technological transformations for the last 6 decades, and the odds suddenly look better.

I’ll start with a 3-4% position, and observe the execution and demand. If they give further confidence, I’ll grow the position.

Hope this helps.

---

That’s all friends!

Thanks for reading Capitalist-Letters!

Please share your thoughts in the comments below.

👋🏽👋🏽See you in the next issue!