It's always a people problem - Why Code Is Never the Real Problem 📖 7 min read

There's this story that gets told in tech, not so much spoken aloud as baked into the way we talk about "engineering" as a profession.

It's the story of the lone engineer.

You can picture it because the industry sells it like a stock photo: the solitary genius hunched over a glowing screen at 2 a.m., headphones clamped on like some ascetic monk, deep in communion with the machine. The world outside is messy and irrational and disappointing, but here, in the clean, syntactically perfect world of code, things make sense.

Input, algorithm, output.

The illusion of pure problem-solving.

This story is seductive because code is predictable. It doesn't misunderstand your words, it doesn't hold grudges, it doesn't need reassurance. If something breaks, you can trace it. You can find the bug. It's the opposite of people, who are all edge cases and undefined behavior. So you start to believe, maybe without even realizing it, that you can spend your whole career here. That if you just get good enough at the algorithms, the data structures, the patterns, you can stay in this pristine world forever.

Except you can't.

Because every line of code you write, every system you build, is ultimately a translation of a human problem.

If you ignore the people, you end up solving the wrong problem.

You don't just bury your head in the keyboard and write code for its own sake. Software is never about the machine. It's about what the machine is doing for someone else. And this is where the first crack in the "lone engineer" myth appears: all the technical brilliance in the world won't save you if you're optimizing for the wrong thing.

Imagine building the most elegant, efficient, O(1) sorting algorithm in history - an algorithm so good it would make Knuth cry. But if no one needs that level of speed, or if your implementation doesn't solve a real user problem, your work is just an academic exercise. Worse, it's irrelevant.

This is where architectural thinking comes in. It shifts the question from "How do I solve this problem?" to "Whose problem is this, why does it matter, and what happens if we get it wrong?"

You can live in the puzzle space for years,focus on the codebase, the tests, the elegant logic. You can say, "I built this feature. It works. Done." And for a while, that's enough. But at some point, the job changes. The problems get bigger. The context gets messier. And you realize that "done" is never just about whether the code passes tests. It's about whether the solution aligns with the messy, contradictory needs of actual humans.

This is where architectural thinking comes in. It's not a title or a role (though it can be) - it's a mindset. It's the ability to step back and see the entire system—technical and human. It means understanding how database choices shape user experience, how business goals influence design, and how one poorly defined requirement can ripple through everything like a bad gene mutation.

Architectural thinking is about asking the questions that don't sound technical at all:

You might argue, "Well, not all problems are people problems. What about scientific software? What about algorithms for particle colliders?"

But even those problems collapse back into human ones. Why are we detecting waves at a collider? Because humans are curious. We want to understand the universe. And when we do, we use that knowledge for human purposes: medicine, energy, space exploration, or just the simple act of knowing.

Even the most abstract code exists because someone, somewhere, asked a human question: What's out there? How does it work? Can we make it better?

So when you think you're solving a purely technical problem, you're still operating in a human context. The algorithm is just the middle layer.

You can build a great career by focusing on nothing but code. You can become the person who fixes things fast, who knows every corner of the codebase, who can optimize an algorithm in their sleep. That depth of skill matters

But sooner or later, the nature of the problems changes. The challenges that shape products and move companies forward aren't just technical. The'’re questions of alignment, clarity, and trade-offs. They require understanding priorities, making calls under uncertainty, and thinking about the system as a whole.

Architectural thinking is about seeing more than the code. It’s about recognizing that systems are made of both machines and people:

This is where Conway's Law hits like a truck: the structure of your software mirrors the structure of your organization. If your teams are siloed and misaligned, your architecture will be too. We must know this. Our role is not just to design for machines - is to design for the humans who will maintain, debug, and extend those machines for years.

At some point, you realize code is not the act of creating a solution. It's the act of translating one. The real solution lives in conversations, diagrams, decisions - long before the first line of code is written.

The best engineers I've worked with aren't the ones who can write 500 lines of brilliant code in a day. They're the ones who can spend hours asking, "What's the real problem here?" and cut the scope by half because they know what actually matters.

Technical ego is the belief that code is the end of the story. That if the user doesn't understand, it's their fault. That if the business team is confused, they're just "non-technical."

But no one cares about your perfect codebase. They care if it works. If it makes their life easier. If it solves their problem. If you can't bridge that gap, your technical skill becomes a commodity - a shiny hammer looking for nails.

The secret skill of an engineer with an architectural mindset isn't knowing every design pattern. It's storytelling.

They build narratives around why the system exists, how it works, and why the trade-offs are worth it. They can explain why the search API needs to return results in under 200ms, not just because "latency is bad," but because every extra second costs $50,000 a month in lost revenue.

This narrative isn't fluff. It's alignment. Without it, every team builds their own version of "the truth," and the system falls apart.

Everything loops back to people. Every bug, every miscommunication, every feature request is a reflection of someone's need or frustration. Even "technical debt" is often people debt - past decisions made without the right conversations or context.

The engineer says, "I solved the problem." The architectural thinker asks, "Whose problem is this, and why does it matter?" The difference isn't technical ability. It's perspective.

You can design the most efficient fan on the planet - optimize its airflow to levels that would make NASA jealous, reduce noise to a whisper, cut energy consumption by 90%. But if you forget that the reason that public buys that fan is simply to keep their beagle from panting in 100-degree heat, you're missing the real problem.

If you want longevity in software, the path isn't just mastering the technical stack. It's learning to see the bigger system - the one that includes people, incentives, and stories.

This shift is uncomfortable because it means giving up the safety of "pure" problem-solving. It means talking to stakeholders. It means listening. It means being wrong. It means the worst nightmare for a software engineer: talking to people.

But it's the only way to go from being a coder to being a builder of systems that last.

It's always people.

This is the truth that hides in plain sight. You see it in failed projects, which almost never fail because of bad code but because of bad alignment. You see it in successful products, which almost always succeed because someone understood the human problem deeply and solved it with empathy and precision.

Once you see this, you can't unsee it. Code is still important - it's the craft, the medium - but it's not the point.

The code is never the goal.

The point is what the code enables, who it serves, why it matters.

So yes, you can spend your career ignoring this, living in the comfort of abstract problems. But if you want to make a larger impact, you have to embrace the mess. You have to talk about the beagle, not just the fan.

Because the fan is easy. The hard part is understanding why it matters in the first place.