Tracing the Arc: My Story of AI

“Every new journey begins with curiosity, a question, and the desire to know something new. My AI journey started in 2019 in the same way evolving through time and discovery, and continuing today with clarity, conviction, and a purpose to serve. This is not a story of expertise, but a story of how I found my way using what I already knew, to learn something new.”

Chapter 1: Inception (2019)

In 2019, I began my journey into AI and deep learning, eager to understand the neural revolution reshaping the world. With a background in system design and engineering, I assumed this would be just another learning curve, challenging, but manageable.

But as I dove into tensors, gradients, and backpropagation, I quickly felt lost. The content was overflowing with knowledge, yet I had no map. Something in my approach wasn’t right.

Reminded of verse from the movie Inception:

"It is not just about depth. We need the simplest version of the idea to grow naturally in the subject's mind." — Inception Movie Link

That was what I was missing, not more detail, but a return to simplicity, to the basics of how I think and how I learn.

The intent stayed with me, quietly growing, until it found a new shape years later.

Chapter 2: Foundation Revisited (2022)

It struck me that I had already encountered the core of AI, not through data science courses, but through College Engineering Mathematics: Linear Algebra, Calculus, and Probability. Back then, these concepts felt abstract, but I had applied them to real problems, solving mechanical systems, modeling thermodynamics, optimizing operations research. Later in my career, I saw the same foundations at work: linear algebra in reconciling settlement transactions across currencies, calculus in optimizing system performance during core banking cloud migrations, and probability in building regulatory data lakes to ensure data quality and compliance.

I could internalize and enjoy these abstractions because they connected to something physical and real. AI felt reachable. I had found a space where I could contribute meaningfully, not by mastering every theory in depth, but by applying trusted concepts in practice, knowing they had already been tested and proven.

This reminds me about another verse from Inception:

"An idea fully formed, fully understood, that sticks right in there somewhere." — Inception Movie link

Chapter 3: A Space in Multiverse (2024)

By late 2024, my exploration of AI had taken on a new dimension, not defined by a single breakthrough, but by a change in how intelligence itself could be organized.

For years, most automation felt like working alone: one system, one process, one set of rules. Even when powerful, they were rigid and fragile, limited to what could be designed, implemented and maintained.

But then, something shifted with Model Context Protocol. Systems no longer had to operate in isolation. They could collaborate easily, agent to agent, system to system exchanging context, extending each other's reasoning, and building on shared capabilities.

What amazed me most was how this collaboration was creating and percieve the world. A broader community of agents was emerging, some designed by others, some self-built, all able to connect and interoperate. Alone, each was simple. Together, they became powerful networks of intelligence.

Suddenly, what once took months to conceive could be prototyped in days. A chain of agents could analyze, interpret, and generate outcomes across domains legal, technical, operational without heavy lifting the usual details.

It felt like stepping into a multiverse: not one AI, but many interacting, complementing, criticizing and multiplying each other's strengths.

It was this realization that led me to incubate SyntropAI, a system that could imagine, reason, and orchestrate cloud services across vendors, policies, and governance layers, using natural language as its interface. It didn't just run. It interpreted. And it evolved.

Reminded verse from the movie Inception, wondering the co-incidence or the deep thinking that has gone behind the movie script around 15 years ago:

Imgaine you are designing a building, conciesly create each aspects. But sometimes it feels like it is creating itself. In (AI) dream, we create and percieve the world. — Inception Movie link

Chapter 4: Embodiment in New Space

Today, I no longer see AI as mere automation. I see it as a collaborator in orchestrating complexity.

The value of AI, however, is directly proportional to the intelligence, of our own or acquired, that we bring to it. At the same time, people often hold AI to impossible standards, wishing it to be flawless while remaining fearful of its potential. Yet we readily accept imperfection in tools, in human decisions, and in teams. The same must be true for AI, human's role in recognizing and miigating those imperfections could become critical.

And when it is, AI stops being a distant technology and becomes something closer, a partner in thinking, a co-architect of systems, and a mirror that helps us see complexity with greater clarity. It is not about replacing our intelligence, but about extending it in ways we can trust.

The Path Forward: Theory to Practice

This journey has been embodied through several AI projects that demonstrate these principles in action:

Each project represents a step toward trustworthy AI systems that don't just execute tasks, but interpret context, respect boundaries, and enable human oversight at every level.

Sometimes the longest path is the one that leads you back to where you started, but with new eyes to see what was always there.