Being Intellectually Lazy: Why One Lens Isn't Enough

On the radiation problem, narrow thinking, and why studying across domains matters more than I thought.

Imagine you’re a doctor treating a patient with an infectious tumor. The tumor cannot be operated on. It must be destroyed, or the patient will die. You have a special ray that can destroy the tumor. At low intensity, the ray is harmless but ineffective. At high intensity, it destroys the tumor but also damages healthy tissue on the way.

How do you save the patient? Think about it.

Don’t feel bad if you can’t solve it. I couldn’t solve it either. 

Let’s look at another story while you think about the tumor problem. 

“A General wanted to capture his enemy’s fortress. He gathered a large army to launch a full-scale direct attack, but then learned, that all the roads leading directly towards the fortress were blocked by mines. These roadblocks were designed in such a way, that it was possible for small groups of the fortress-owner’s men to pass them safely, but every large group of men would initially set them off. Now the General figured out the following plan: He divided his troops into several smaller groups and made each of them march down a different road, timed in such a way, that the entire army would reunite exactly when reaching the fortress and could hit with full strength.”

Do you have an answer to the tumor problem now? It’s a bit embarrassing to admit that I couldn’t get it even after reading the general’s story. 

When psychologists Gick and Holyoak presented this problem to participants, only 10% could solve it immediately. After reading the general’s story, 30% figured it out.

But what’s fascinating is that when they were explicitly told to use the story as a hint, 75-92% solved it.

Fortress is analogous to the tumor. The large army corresponds to the high-intensity ray. Small groups of soldiers represent low-intensity rays. Once you see the pattern, the mapping is obvious – but only in hindsight.

Image: Duncker’s Radiation Problem by Blacktc

So, the solution is to split the ray up and direct multiple rays from different angles so they all reach the tumor together. Each individual ray is low intensity - harmless to healthy tissue. But when they converge at the tumor, they collectively deliver high intensity and destroy it.

The breakthrough wasn’t intelligence. It was analogical reasoning - recognizing that a solution from one domain (military strategy) could apply to another (medical treatment).

It’s naturally difficult to think beyond step one. We often do not look for connections outside our immediate domain.

I read this story in a remarkable book called Range, written by David Epstein about active open-mindedness and analogical reasoning.

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Watching Someone Who Actually Does the Work

Earlier this year, I attended Prof. Sanjay Bakshi’s “Cases in Business and Investment Analysis” program at Flame University.

Prof. Bakshi (pen name Fundoo Professor) doesn’t need an introduction. I’d read his blogs before, so I knew what to expect – or thought I did. But what left me awestruck wasn’t his track record. It was watching someone who’s already proven everything still work with that level of curiosity and intensity. Crossing boundaries, connecting domains most investors wouldn’t explore. Finding robust business models in companies the market misunderstands and systematically avoids.

What increased my respect even more: his willingness to accept mistakes openly. Here’s someone with numerous multibaggers, yet constantly focused on improving the process. Not chasing returns, just relentlessly getting better.

Watching him dissect business cases, I felt stupid. Not because my returns haven’t been great. But because I realized I haven’t been rigorous and curious enough.

I’d been hiding behind “Buy quality at reasonable prices and hold them.” It sounds sophisticated. But little did I realize that it had become an excuse for intellectual laziness.

And just like the radiation problem, I’d been studying opportunities with only one lens – not looking for connections outside my immediate domain.

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What I Was Missing

My approach wasn’t fundamentally wrong. But I’d turned good principles into rigid rules that filtered out entire categories of opportunity.

I’d spent too much time studying behavioral finance, imposing that lens on everything. Every opportunity got filtered: “Is this a quality business at a reasonable price?” If not, dismissed.

Worse, I’d become obsessed with avoiding risk. I’d fold my hands and wait. But the work of studying businesses shouldn’t stop just because valuations are elevated. You’re not building knowledge for next quarter – you’re building it for the next decade.

Youngme Moon, a prof. of Business at Harvard Business School writes in her book Different:

“Once we over learn something, we cease to know it anymore at all.”

She continues, “I have come to believe that a poem perfectly memorized is a poem too easily recited….And a poem performed without effort is a poem that has lost all meaning.”

That hit hard. I’d over-learned the quality investing playbook so thoroughly that I’d stopped actually thinking. Among others, I failed to study -

Companies in transformation: Businesses moving from mediocre to good. Management fixing capital allocation or reducing debt. Operational improvements that don’t show up in recent financials. These don’t fit neat quality checklists. I was studying balance sheets when I should have been studying inflection points.

Value migration: Sometimes the opportunity isn’t picking the best horse – it’s recognizing that the whole stable just got faster. Regulatory changes, government push, technology adoption, demographic shifts that expand profit pools dramatically. When such tailwinds hit, traditional valuation metrics make less sense. Even average companies with decent management get a massive shot in the arm.

I was like the doctor staring at the tumor with one type of ray, unable to imagine that solutions might require perspectives from completely different angles.

Why Learning Isn’t Linear

There’s a phrase that gets thrown around a lot in investing: “let it compound.” The implication is that if you just keep doing what you’re doing, stay consistent, don’t deviate, you’ll get there eventually.

But great compounding stories aren’t linear. They include step changes.

Amazon wasn’t just a bookstore that grew steadily. It made leaps. Books to everything. Everything to AWS. Each was a step change.

Why should learning be any different?

If I keep studying businesses through the same lens, I’m just getting incrementally better at a narrow skill. But if I study across domains - I’m building a library of patterns that can converge when I encounter something new.

That’s the radiation problem applied to investing.

You need multiple perspectives from different angles to see solutions that aren’t visible from a single vantage point.

My process hasn’t been good. I’ve been too static, too cautious, too narrow.

Annie Duke has this line in Thinking in Bets that keeps coming back to me:

“What makes a decision great is not that it has a great outcome. A great decision is the result of a good process, and that process must include an attempt to accurately represent our own state of knowledge.”

So what’s changing? I’m studying more ideas – across geographies, across business models, across industries I’ve ignored.

Will most lead to investments? No. But i hope each one adds to the pattern library.

I’m also changing where I spend my time. Less Twitter commentary, fewer videos of investment managers. More conversations (through podcasts, interviews, books) with operators, business owners, scientists – people actually building things. They understand what makes businesses work in ways analysts seldom do.

And critically: the humility to study businesses deeply without needing to act immediately. Not every analysis needs to end in a trade. But every analysis should end with learning something new.

Will this work? I don’t know. The outcome isn’t guaranteed – it never is in investing. But the process of improvement is what I can control. And right now, that process demands more ambition, more curiosity, and more hard work.

The Only Edge That Matters

I started this post with the radiation problem because it captures what I’d been missing. Most of us can’t solve it not because we’re unintelligent, but because we’re trapped in a narrow view.

I’m more motivated now than I’ve been in years. Not because I’ve had some great insight that’s going to 10x my portfolio. But because I’ve finally admitted I was coasting. That I’d mistaken “not making mistakes” for “doing good work.”

Munger was right. The only edge that truly compounds is learning. Everything else – the returns, the track record, the reputation – those are just outputs of how good a learning machine you are.

One ray wasn’t enough to solve the tumor problem. One lens won’t be enough for investing either.

The work starts now.