How To Think Like A Great Investor

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Dear Friend,

Happy Wealth Wednesday.

One of the biggest differences between average investors and great investors is how they think. Most people react to what is right in front of them. The best investors think two moves ahead.

That is what second-order thinking is all about. It is the ability to look past the obvious and think about what comes next. Not just what happens, but what happens because of what happens.

When I first started investing, I focused too much on headlines. A company misses earnings, a stock drops, or a CEO resigns, and I would react. But over time, I learned that reacting is not how you build wealth. The real money is made by slowing down and asking better questions.

Second-order thinking helps you see what others miss. How will this event change behavior? What ripple effects will it create? Which companies could quietly benefit while everyone else panics?

In this week’s Wealth Wednesday, I want to walk you through what second-order thinking really means, how Warren Buffett used it to make one of his best investments ever, and how I’ve applied the same idea in today’s markets, including when I sent out Oklo Energy at $18 and Nebius at $30.

The Difference Between First-Order and Second-Order Thinking

Most investors think in straight lines. Something happens, they react, and that’s the end of it.

That is first-order thinking. It is the most common way people make decisions in the market. A stock drops, they sell. A company reports strong earnings, they buy. It is simple, emotional, and usually wrong over the long run.

Second-order thinking is different. It is slower and more thoughtful.

Instead of asking “what just happened,” you ask “what happens next because of this.”

The best early example of this comes from Warren Buffett and American Express.

In the early 1960s, a company that borrowed money using tanks of salad oil as collateral was exposed for fraud. Those tanks were mostly filled with water. American Express had guaranteed the loans, and when the scandal broke, investors thought the company was finished. The stock crashed.

That is what most people did. They saw a scandal and assumed it meant the business was broken.

Buffett thought differently. He asked a deeper question: does this scandal actually change how people use American Express? Are customers still swiping their cards? Are businesses still accepting them?

Instead of guessing, he went into the real world to find out. He walked into restaurants and watched customers pay. They were still using American Express cards like nothing had happened.

That told him everything. The scandal hurt the company’s reputation temporarily, but not its business model. The trust, convenience, and habit of using the card were still intact.

Buffett bought aggressively while everyone else was selling. Years later, the stock recovered and multiplied. That was second-order thinking in action.

It was not about reacting to the news. It was about understanding what the news actually meant for the company’s future.

Buffett turned $13 million into a 206% gain in about 18 months for a massive return.

Now let’s fast forward to something more recent.

When Russia invaded Ukraine in 2022, the first thing that happened was a massive disruption in natural gas supply to Europe. Russia had been Europe’s main energy supplier, and overnight, that pipeline was gone. Prices of natural gas spiked, and Europe scrambled for alternatives.

Most investors jumped to the same conclusion: buy U.S. energy exporters. And it worked. The big energy companies soared. That was first-order thinking.

But second-order thinkers asked, “what else must be true for this to work?”

If Europe is suddenly importing gas from across the ocean, that means it needs a lot more ships to transport it. LNG shipping rates hit record highs because there simply were not enough tankers. Investors who recognized that early made a fortune in shipping stocks before the rest of the market caught on.

Then came the next ripple. Even if Europe could import all that gas, it still needed to process it. Countries like Germany had no regasification terminals at the time. So they rushed to build floating terminals and expand ports. Engineering and construction companies quietly benefited from that wave.

And it did not stop there. The energy crisis pushed fertilizer factories across Europe to shut down because natural gas is a key ingredient in ammonia production. Fertilizer prices exploded, and North American producers became the unexpected winners.

All of that came from one event.

Second-order thinkers connected the dots. They looked past the first obvious play and saw the ripple effects that would unfold over months and years.

That is what separates investors who chase moves from those who create them.

First-order thinkers react to headlines. Second-order thinkers question them. They ask what happens next, who benefits indirectly, and what parts of the story are still being ignored.

The deeper you think, the fewer competitors you have.

Applying This Framework to the AI Revolution

I will walk you through how I use second-order thinking to approach investing in AI.

Full disclosure, I personally invested my money and sent out premium stocks in the infrastructure buildout instead of the Nvidia supply chain.

I will note each time I sent out a premium investing pick.

When ChatGPT launched in late 2022, the obvious investment was Nvidia. Everyone saw it. The company designs the GPUs that power artificial intelligence. But I wanted to go deeper. If Nvidia was the obvious play, what else had to happen for their success to continue?

Nvidia designs the chips, but they do not manufacture them. That work is done by Taiwan Semiconductor Manufacturing Company (TSMC), which builds chips for almost every major tech company on the planet. So if Nvidia’s demand was exploding, TSMC’s production capacity would be the next critical point in the chain.

Then I asked another question. What does TSMC need to make these chips? That answer led to ASML, a Dutch company that builds the advanced lithography machines used to print the tiny circuits on each chip. These machines are so complex that only ASML can produce them at scale. Without ASML, the entire semiconductor industry would stall.

From there, I looked even further down the chain. High-end chips require rare earth elements like neodymium, dysprosium, and gallium. These materials are essential for magnets, lasers, and the tiny components inside GPUs. The problem is that most of these minerals come from a handful of countries, which creates geopolitical risk.

That is where MP Materials comes in. They are one of the few companies in the United States mining and processing rare earths domestically. The world is realizing that these minerals are not rare because they are hard to find, but because they are hard to refine. Building a resilient supply chain starts with companies like MP that reduce our dependence on foreign processing.

Once I understood the supply chain, I asked where all these chips were going. Every AI model needs a home, a place to train, process, and store data. That home is the data center.

Data centers are the physical backbone of the AI revolution. They are not just rooms of computers. They are massive, specialized ecosystems designed for nonstop computing. Each one houses thousands of GPUs and requires precise cooling, stable power, and advanced networking to run efficiently. When you type a prompt into ChatGPT or use an AI feature on your phone, that request runs through these data centers, not your local device.

Demand for new facilities has exploded since 2022. Microsoft, Amazon, and Google are building them faster than ever, but they still cannot keep up. This created a huge opportunity for companies focused purely on building and operating these facilities.

That is what led me to Applied Digital. They do not create AI models. They build the infrastructure that makes those models possible. Their data centers are designed for AI workloads, with faster networking, liquid cooling, and direct connections to efficient power sources.

I sent out Applied Digital to premium members at $4 and it is currently around $31.

But the story does not end with real estate. Once thousands of GPUs are packed into a single facility, they need to communicate and share workloads efficiently. That is where companies like Nebius come in. They build the connective software and infrastructure that allow AI clusters to operate at scale. Without this layer, AI systems would bottleneck, wasting energy and capacity.

I sent out Nebius to premium members at $36 and it is currently over $100.

The more I studied data centers, the more I realized their biggest limitation was not just space or hardware. It was energy.

AI data centers consume enormous amounts of power. One large data center can use as much electricity as an entire city. To support that growth, we need new sources of clean, reliable power that can run around the clock. That led me to nuclear energy.

Nuclear power is one of the few carbon-free sources that can produce continuous electricity at scale. New small modular reactors are cheaper, safer, and faster to deploy than traditional plants. This technology has the potential to become the future energy source for AI infrastructure.

That is why I started looking at Oklo. The company focuses on advanced nuclear designs that will provide steady energy directly to data centers and other industrial users.

I sent out Oklo to premium members at $18 and it is currently around $100.

When you put it all together, this is what second-order thinking looks like in practice. Nvidia was the obvious starting point, but each step required asking one simple question: what else must be true for this to work?

Chips led to materials. Materials led to manufacturing. Manufacturing led to data centers. Data centers led to power. And soon, power will lead to water, because cooling AI infrastructure already consumes millions of gallons each day.

That is how I approach every major investing theme. Instead of stopping at the headline company, I map out what has to exist next and position before the market catches on.

1. Identify the obvious mega-trend (AI boom)

2. Accept that the consensus stock (Nvidia) is already known

3. Ask what must happen next to make that trend work

4. Find the supply-chain chokepoint in energy, manufacturing, or materials

5. Research which companies solve that chokepoint

6. Position ahead of institutional recognition

The Next Bottleneck: Water

In my opinion, the next major bottleneck for AI isn’t chips or power. It’s water. Every large data center needs massive cooling systems to keep thousands of GPUs from overheating, and those systems rely on millions of gallons of water every single day.

It’s now projected that by 2027, data centers will use as much water in one year as the entire United Kingdom. That number is shocking, but it makes sense when you look at where these facilities are being built. Most new data centers are going up in areas with large amounts of open land, which also tend to be regions where water is hardest to get.

This is setting up a major problem. Cities, farms, and technology companies are all competing for the same limited supply. And when something this essential becomes scarce, it creates both a crisis and an opportunity for investors who are paying attention.

That’s why I’ve been researching companies building advanced water recycling and cooling systems for AI infrastructure.

Conclusion

Second-order thinking isn’t about guessing the future. It’s about connecting the dots faster than everyone else. The investors who win big in the next decade won’t just chase the obvious AI names. They’ll study what’s required to make that world possible, like the energy, materials, infrastructure, and even water that power it all.

That’s how I approach every major trend. I ask what has to happen next, who benefits from it, and where the real bottlenecks are forming. It’s not luck. It’s patience, logic, and a willingness to look deeper than the headlines.

If you start thinking this way, you’ll see opportunities most investors completely miss. Because the truth is, by the time the story hits CNBC, the trade is already over.

I’ll be sending out my next premium report tomorrow on the company working to solve the water crisis inside AI data centers. If you want early access, make sure you’re on the list.

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