The Intuition Moat
Why your "gut feeling" is actually just rapid, subconscious causal reasoning and why AI can't replicate it
If you look at the job market right now, you might notice something specific happening. It isn’t just that companies are hiring fewer people. It’s that they have stopped valuing a specific kind of work.
For the last decade, if you could pull data and put it into a chart, you were safe. We called this being “data-driven.”
But companies are slowly realizing that being data-driven on a noisy foundation is dangerous. They have plenty of charts. What they are missing is the truth behind them.
We are shifting from an economy that rewards Pattern Finding to one that rewards Causal Thinking.
The Difference
Most people use “correlation” and “causation” interchangeably. In business, they are opposites.
Correlation is “What Happened.”
You see that on rainy days, umbrella sales go up.
You see that when you send more emails, revenue goes up.
AI excels here. It can scan millions of rows of data to find these relationships instantly.
Causation is “Why It Happened.”
Did the rain cause the sales? Yes.
Did the email cause the revenue? Maybe not.
Consider a product team analyzing feature usage.
The Pattern: Data shows that users who configure “Advanced Settings” retain 3x longer than those who don’t. The dashboard implies a clear win: Force every new user to configure settings during onboarding.
The Reality: The strategy fails. The settings didn’t cause the retention. The user’s high intent caused both.
Only people who already loved the product bothered to customize it. The feature was a filter, not a factory. When you mistake the two, you waste months engineering “onboarding flows” that force casual users to act like power users, annoying everyone and fixing nothing.
The Human Fortress: Structured Intuition
There is a fear that AI will replace all analysis. This misunderstands what analysis is.
AI is a prediction engine. It assumes the future will look like the past. It sees the noise, but it cannot see the context. This is where Intuition enters the equation.
We often treat data and intuition as enemies. “Don’t trust your gut, trust the data.” This is wrong. Good intuition is actually just rapid, subconscious causal reasoning.
When a CFO looks at a forecast and says “that looks too high,” they aren’t guessing. They are sensing a hidden variable the model missed.
When a marketer says “customers won’t like this copy,” they are predicting a human emotional reaction that no dataset can capture.
Causal thinking doesn’t replace your intuition; it sharpens it. It gives you the vocabulary to explain why your gut is right. It allows you to say, “This correlation is a mirage because of X factor.”
AI can process the “what.” Only a human can intuit the “why.”
The Universal Language of Truth
This isn’t a new invention for business. We are just finally catching up to the rest of the serious world.
If you look outside our industry, you see that the “invisible rules” of causality govern everything that matters.
In Medicine: Researchers don’t just observe that patients taking a pill get better. They run randomized trials to prove the pill caused the healing, separating the cure from the placebo.
In Law: A prosecutor cannot just show a suspect was at the scene. They must prove beyond a reasonable doubt that the suspect’s actions caused the crime.
In Economics: Experts look for “natural experiments” to see how policy changes actually shift human incentives.
We have been playing by different rules in business - accepting “bent rulers” and comfortable half-truths because the numbers looked nice on a slide. That era is ending.
The Path Forward
This shift - from observing patterns to understanding cause - is the most transferable skill you can learn.
It applies to marketing, yes. But it also applies to leadership, product design, and personal growth. Once you learn to see the difference between a signal and noise, you cannot unsee it.
Over the coming weeks, we are going to explore these “invisible rules.” We will look at how to think like an epidemiologist when diagnosing a business problem. We will look at how to think like a judge when weighing evidence.
The goal is not to become a statistician. The goal is to nurture a specific kind of curiosity.
It’s the curiosity that refuses to accept the easy answer. The instinct that asks, “Is this true, or does it just look true?”
In a world drowning in data, this clarity is the only competitive advantage that lasts.
- Talgat
