From Excel to AI: Structure, Questions, and Becoming Who You Are

My journey as a programmer began unexpectedly.

It wasn't through code or coding classes; instead, it was in response to a more profound question – one that has been asked of me repeatedly throughout my life.

As a young person, I've often found myself at the center of conversations, with others seeking my perspective on matters ranging from politics and culture to life-altering decisions. Despite my age, I've frequently been the voice of reason in rooms filled with seasoned adults, teachers, friends, and colleagues.

While I may not have had a clear understanding of why this was happening, one thing became apparent: I possessed an innate ability to make sense of complex ideas and navigate unfamiliar territory. This skillset would eventually become the foundation for my career in programming, as I transitioned from Excel spreadsheets to the world of Artificial Intelligence.

In reflecting on my journey, I've come to realize that this unique experience has not only shaped me into a programmer but also taught me valuable lessons about embracing authenticity and finding structure in an ever-evolving field. As I continue to grow and evolve alongside AI, I'm reminded that true fulfillment lies not in the technology itself, but in understanding its potential to illuminate our humanity.

Years later, I was a government major interning in the California Legislature. My job wasn’t glamorous. It wasn’t tech. It wasn’t strategy.

It was Excel.

At the time, it felt administrative. Mechanical. Almost boring.

But looking back, that spreadsheet was my first real experience with coding.

Because coding isn’t mysterious symbols on a black screen.

Coding is structured thinking.

It’s deciding what matters and what doesn’t.

It’s creating consistent fields so chaos becomes searchable.

It’s turning paragraphs into variables.

It’s separating signal from noise.

When I highlighted the pros and cons of a bill, I was performing classification.

When I standardized language across hundreds of letters, I was normalizing data.

When I reduced emotional testimony into bullet-point arguments, I was extracting features.

I didn’t call it machine learning. I didn’t call it data architecture. I just called it doing my job.

But in today’s AI-driven world, those same principles power intelligent systems.

AI does not thrive on chaos. It thrives on clean inputs. Clear categories. Repeatable logic. Consistent formatting. The same discipline I learned logging legislative mail is the discipline required to build reliable machine learning systems.

And maybe that’s why people asked me questions all those years.

Not because I had all the answers — but because I instinctively tried to organize the problem before reacting to it.

I look for categories.

I look for definitions.

I look for the columns before I fill in the rows.

So when I build software now — whether I’m structuring CSV pipelines, designing AI prompts, organizing metadata, or building tools that learn from information — I return to the same principle I learned in that legislative office:

If a human can’t clearly categorize it, a machine definitely can’t.

My coding philosophy didn’t begin in a computer science lab.

It began with questions.

It was refined in spreadsheets.

And in a strange way, the more “boring” the data looks, the more powerful the system becomes.

Because underneath every intelligent machine is something very simple:

A clean table.

A clear structure.

And someone willing to ask the right questions first.

It was only later that I discovered my true passion lay not in the world of politics or social justice, but in the realm of technology - specifically, computer programming. As I delved deeper into the world of coding, I began to realize that this journey was not just about mastering a skillset, but about becoming the person I am meant to be.