What I Learned from Studying 100 Investment Decisions

Last year, I set myself a project: study 100 documented investment decisions made by Buffett, Munger, Howard Marks, and other investors I respected. Not their philosophy -- their actual decisions. What they bought, when, why, and what happened.

I'm a software engineer, not a fund manager. But I had a hypothesis: the decision-making patterns of great investors would transfer to engineering, product, and career decisions. After 100 case studies, I can confirm: the overlap is almost complete.

Here are the seven patterns that showed up most consistently.

Pattern 1: The Best Investors Do Nothing Most of the Time

Of the 100 decisions I studied, the most striking insight wasn't about the decisions they made -- it was about the decisions they didn't make.

Buffett reviews hundreds of potential investments per year and acts on maybe two or three. The rest are rejected, often within minutes. His metaphor is baseball: unlike actual baseball, in investing you don't have to swing. You can wait for the perfect pitch.

In engineering, this translates to discipline about what to build. The best technical leaders I've worked with reject most feature proposals, most "quick wins," and most refactoring urges. They wait for the high-value, high-conviction opportunities and then commit fully.

The impulse to stay busy -- to always be shipping something -- is one of the biggest destroyers of value in engineering organizations.

Pattern 2: Strong Opinions on Competence Boundaries

In nearly every studied case where an investor lost big, they were operating outside their circle of competence. Buffett's investment in Dexter Shoes. His late entry into airlines. Munger's early real estate deals.

When they stuck to what they genuinely understood -- consumer brands, insurance float, simple businesses with durable advantages -- the hit rate was extraordinary.

I mapped this to my own career. My biggest engineering mistakes all involved overconfidence in unfamiliar territory: attempting a custom ML pipeline when I should have used an off-the-shelf solution, designing a distributed system when a monolith would have been fine, choosing bleeding-edge technology because it was exciting.

The decisions where I respected my competence boundaries -- using boring technology I understood deeply, building straightforward systems with proven patterns -- almost always worked.

Pattern 3: They Seek Disconfirming Evidence

Darwin had a habit: whenever he encountered a fact that contradicted his theory, he wrote it down immediately. He knew his brain would try to forget it.

The best investors do the same thing. Before making an investment, Munger actively seeks reasons NOT to invest. He calls it the "kill the idea" phase. Only ideas that survive aggressive attempts to disprove them get funded.

In engineering, this maps to adversarial testing of ideas. Before committing to an architecture:

• What are the strongest arguments against this approach?
• Who on the team disagrees, and why?
• What would have to be true for this to fail?

I now assign a "red team" role in architecture reviews. Someone's explicit job is to argue against the proposal. The proposals that survive are much stronger.

Pattern 4: Simple Beats Clever

Of the 100 decisions I studied, the simplest ones had the highest success rate. Buffett buying Coca-Cola stock (people will keep drinking Coke). Munger's Costco investment (lowest cost wins). See's Candies (strong brand, pricing power, minimal capital needs).

The complex, clever investments -- the ones requiring multi-step theses about macroeconomic shifts, regulatory changes, and technology adoption curves -- had much worse outcomes.

In engineering: boring technology works. Simple architectures win. The clever microservices design with event sourcing and CQRS might be intellectually satisfying, but the PostgreSQL monolith ships faster, breaks less, and is easier to debug at 2am.

Complexity should be adopted reluctantly, not enthusiastically.

Pattern 5: They Size Bets Based on Conviction

When Buffett is confident, he doesn't diversify. He concentrates. His top five holdings often represent 70%+ of his portfolio. This is the opposite of what most financial advisors recommend.

The logic: if you've genuinely done the work to understand something deeply, spreading your bets is a hedge against your own analysis. If you don't trust your analysis, the right move isn't to diversify -- it's to do more analysis or abstain entirely.

For engineers: when you've done the work to validate an approach -- load tested, prototyped, consulted experts, red-teamed the design -- commit to it fully. Don't hedge by half-implementing alternatives or building excessive abstraction layers "just in case." Go all in on the approach you've validated.

Pattern 6: Time Horizon Is a Competitive Advantage

Nearly every great investment in my study had a multi-year time horizon. The investors weren't trying to make money this quarter. They were positioning for the next decade.

This patience is itself an edge, because most market participants are forced to think short-term. Fund managers face quarterly redemptions. Public company CEOs face quarterly earnings calls. The person who can think in five-year increments has an enormous structural advantage.

In engineering: the team that invests in platform quality, developer experience, and technical foundation -- even at the cost of short-term feature velocity -- wins in the long run. Every engineering leader knows this intellectually. Few have the organizational backing to actually do it.

Pattern 7: Post-Mortems Are Non-Negotiable

Every investor I studied maintained detailed records of their decisions and outcomes. Not just the wins -- especially the losses. Buffett's annual letters contain frank analysis of his mistakes. Marks writes detailed memos after every major decision.

This is the decision journal applied at scale. The investors who improve over time are the ones who systematically study their errors. The ones who don't keep records repeat the same mistakes for decades.

The Meta-Lesson

After 100 case studies, the meta-lesson is this: great decision-making is not about intelligence. It's about discipline, humility, and a commitment to systematic thinking.

The patterns repeat: wait for high-conviction opportunities, stay within your competence, seek disconfirming evidence, keep it simple, commit when you're sure, think long-term, and study your mistakes.

These principles aren't secrets. They're just hard to practice consistently. If you're interested in exploring these investment decision-making principles in a structured format, the principles collection on KeepRule organizes them by theme in a way that makes them practically usable.

Where to Start

Pick one pattern that resonates. Apply it to a decision you're facing this week. Write down your reasoning. Review it in three months.

That's how these investors built their judgment: one deliberate decision at a time, over decades.

You can start today.