The Problem With Promising Fields

When I finished my Master’s thesis on machine learning, I didn’t feel like an expert. I felt like someone who’d spent years learning the rules of a game that keeps changing its playbook. ML isn’t unique here—many emerging fields share this trait. But here’s the catch: When a discipline is young, its promise often outpaces its practicality. Let me explain why that matters.

The Experience Paradox

Job postings in ML have a curious quirk. They demand 3–5 years of specialized experience for entry-level roles—a paradox familiar to anyone in a hot field. It’s like requiring a driver’s license to take driving lessons. Fresh graduates face a trap: You need experience to get experience. In established fields like software engineering, internships and junior roles act as bridges. In ML, the bridge is half-built.

The Identity Crisis

In software engineering, titles mean something. A “backend developer” designs servers; a “frontend engineer” builds interfaces. In ML, job descriptions blur. One company’s “ML engineer” builds pipelines; another’s “data scientist” does the same work but calls it analysis. This isn’t just confusing—it’s a red flag. When roles aren’t defined, skills become scattered. You end up learning a little of everything but mastering nothing.

The Mentor Gap

Every field has its elders—those who’ve survived its evolution. Software engineering has them in spades: engineers who debugged mainframes, scaled early web apps, or survived the JavaScript framework wars. Their stories become guidebooks. ML, though, is too new for that. Its pioneers are still in the field, not reflecting on it. Without veterans to warn you about dead ends, every problem feels uncharted.

The Art of Guessing

ML has a dirty secret: Much of it is trial and error. You tweak hyper-parameters, swap algorithms, and cross your fingers. It’s less like engineering and more like composing jazz—improvisation within structure. That’s thrilling for some. But if you prefer systems with clear cause-and-effect—like debugging code or optimizing databases—the ambiguity grates.

The Treadmill Effect

In software engineering, a tool like Python or SQL stays relevant for decades. In ML, yesterday’s breakthrough is today’s footnote. New papers drop weekly; frameworks pivot yearly. Staying current isn’t just work—it’s a second job. For some, that’s energizing. For others, it’s exhausting. Careers are marathons, and sprinting indefinitely isn’t sustainable.

The Strategic Pause

Here’s what I realized: Mastery requires stability. Software engineering offers that. Its problems are well-scoped, its roles defined, its tools enduring. By building expertise there first, you gain something ML rarely provides—a foundation. Foundations let you pivot later without starting from zero.

This isn’t quitting. It’s prioritizing. Fields like ML reward those who can endure uncertainty. But uncertainty has a cost. Right now, I’d rather invest in skills that compound predictably. The cutting edge will still be there—but I’ll approach it from solid ground.

The Takeaway

Emerging fields dazzle us with potential. But potential isn’t the same as opportunity. Sometimes, the wiser bet isn’t what’s possible, but what’s repeatable. And right now, repeatable beats revolutionary.