A Path Not Chosen

In the early days of college, I was surrounded by an overwhelming array of technologies, each promising a future brighter than the last. It was a marketplace of hype, with everyone chasing what seemed most in demand fields buzzing with job postings, headlines, and trends. I quickly found myself unmoved. It wasn’t that I didn’t care; I simply couldn’t muster any excitement for the well-trodden paths that drew others so effortlessly. Competition felt like noise, and mainstream pursuits app development, cloud computing, or the latest in web frameworks held no appeal. I wasn’t interested in adding my voice to a crowded room.

So, I drifted. I tried on different fields like masks, exploring everything from app development to cloud computing. Each one felt like a series of recycled ideas, the same patterns, the same tools, as if no one wanted to question the status quo. Nothing stood out. None of it resonated with any real substance, and a sense of repetition followed me in each exploration.

Then, by some twist of fate, I stumbled upon machine learning. It wasn’t love at first sight. In fact, I resisted it initially. The thought of diving deep into math, algorithms, and complex statistics felt like signing up for endless frustration. Mathematics had never been something I enjoyed, and machine learning demanded more than just casual interest; it was rooted in dense, relentless math, and I’d spent years avoiding it. But the irony was clear what I’d avoided for so long was the one thing that held my attention in a way that no other field had managed to do.

Something about the complexity, the lack of pre-packaged answers, intrigued me. Machine learning wasn’t a playground filled with trendy frameworks or popular tools that everyone knew; it was raw and demanding. I could feel the weight of its intricacies, and it was as though the field itself was indifferent to whether people liked it. There were no shortcuts. And I was drawn to that.

From my third semester onward, I dove into machine learning, immersing myself in its depths. I scoured courses on Udemy, burned through hours of YouTube tutorials, and combed through dense books that dissected algorithms down to every formula and assumption. There was no one guiding me but myself. Each lesson was another solitary step into a labyrinth of complexity, yet I kept going. I started building projects, each one more challenging than the last. With every completed model, I felt a strange sense of progress a sense of anchoring in something that didn’t rely on the trends that ruled other fields.

Over time, my efforts translated into real projects. I gained experience as an AI intern, working on models that tackled real-world problems. It was far from glamorous, but it was real. I thought, finally, I’d found something that set me apart. Each problem I worked through, each setback I encountered, brought me further from the crowd, cementing me in a field that felt foreign to everyone around me. Machine learning became a quiet passion not the kind that announces itself, but the kind that grows over time, creeping in until it’s the only thing left.

Yet, passion doesn’t pay. When the time came to apply for jobs, I faced a rude awakening. I crafted resumes, tailored cover letters, and poured every detail of my work and dedication into my applications. And still, the rejections came. One after another, a string of polite dismissals that chipped away at my confidence. Despite my projects, my internship, and the knowledge I’d acquired, companies weren’t interested. The market seemed indifferent, and my efforts, no matter how carefully curated, were overlooked. My passion felt irrelevant.

In desperation, I reached out to mentors, hoping they could point me toward something I’d missed, some missing piece that would bridge the gap between my work and the opportunities I sought. But their advice was less guidance and more resignation. “Maybe it’s time to consider other fields,” they hinted. Teachers echoed the same sentiment, nudging me gently, and sometimes not-so-gently, toward different avenues. Seniors shared their own stories of pivoting, explaining how “following the market” was part of the game. They all seemed to agree that I’d invested too much in something that wasn’t going to pay off.

Pressure has a way of unraveling even the strongest convictions. Despite my years of commitment, the weight of others’ expectations, the market’s indifference, and the looming threat of continued rejection pushed me to a breaking point. Gradually, it became clear that I didn’t have the luxury of pursuing what I wanted. Survival in this industry meant letting go of ideals and adapting to what was profitable, predictable, and, above all, marketable.

Now, my options have narrowed to a path I once rejected entirely a field I used to dismiss as unoriginal, saturated, and uninspired. It’s not a shift I wanted; it’s a shift I’m being forced to make. It’s ironic, really, that after all the work I put into avoiding the well-beaten paths, I find myself staring down one that couldn’t feel more foreign. The field I’m now preparing for is crowded and familiar, a place where everyone seems to already have a foothold. The once-comforting syntax of Python is no longer enough, and I’m left to confront a new language, a new paradigm that feels as unnatural as it does obligatory.

This isn’t a choice; it’s an adaptation. I’ll be stepping into a domain I never wanted, forced to mold myself into something I don’t yet understand. My passion has been muted, overridden by necessity. In the end, it’s not about what I wanted; it’s about what the job market demands. And sometimes, that’s all there is.