The Short Story of Column X (and the Domain Thinker Who Could Have Saved It)

It all started with an innocent request:

"Let's add a column X to the table. We'll need it for a new feature."

Just a column. Nothing special. Quiet, humble, placed at the end of an existing table.

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Then came the first variation:

"X is optional, but only for certain users."

So we added a condition.

After that:

"We need to track changes."

And that’s when exceptions, flags, and fallbacks started asking us:

Who was X, and what was it supposed to do?

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Then again:

"X should follow some validation rules related to another process."

The kind of rules you won’t find written anywhere—only passed down orally from generation to generation of product owners, somewhere between a demo, a meeting, and a casual:

“I’ll explain it to you real quick.”

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At that point, it happened:

X wasn’t just a column anymore.

It had grown up.
It had business logic, responsibilities, rules, and its own lifecycle.
It no longer looked like a simple field—it demanded an identity.

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At that point, I said to myself:

"Maybe, in the beginning, I should have asked myself one more question."
"But X… what does it really represent in our context?"

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A question that a Domain Thinker would ask.
It’s not an official role, but an attitude—a mindset for approaching problems:
One that doesn’t start from tables or classes, but from meaning.

Someone who asks themselves:

• Does this information really make sense here?

• Could it grow?

• Will it all break in six months?

As Eric Evans wrote:

"A model is not just a set of classes."

And sometimes, it’s not just a column either.

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With AI increasingly helping us write code, thinking—and above all, analyzing—before writing has become a fundamental skill.

It always has been, but now it's more evident than ever.

Code can be generated by anyone.
But thinking about what makes sense to model—that requires thought, context, and a pinch of healthy paranoia.

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Here comes the crucial point, in my opinion:

If we don't invest in Domain Thinking—modeling before implementing—
then we risk reducing our role to that of a mere executor.

In other words:

If we remain passive, writing code without caring about the deeper meaning of the system,
AI—which can generate code at record speed—could truly replace the coder.

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We, as professionals, must focus on the value that only abstract thinking, experience, and domain understanding can provide.

And maybe, in an IT market that tends to value the most visible skills—technologies, tools, stacks (all very important things)—
we should start giving more recognition to those who know how to analyze, abstract, and model.

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Because that's where the difference lies:

Between software that works...
...and software that still works two years from now.

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So next time someone proposes adding a column...
ask yourself:

What if it were the star?