As I reached my thirties in my career, I realized that system crashes, full disks, or network outages weren’t actually my biggest problems. The real killer was the silently accumulating technical debt, brushed aside with “we’ll fix it later.” I’m talking about a monster that insidiously grows deep within a project, incurring more and more costs every day.
This reality became very clear to me, especially as someone who has worked for years in system architecture, network management, and enterprise software development. I’ve seen that, most of the time, software architecture is a reflection of organizational flows and instantaneous decisions, rather than just lines of code.
The True Cost of Saying “Yes”
When working on a manufacturing ERP, how easy it was to say “yes, we can do it” when a new feature or report was requested. Customer satisfaction, pressure for quick delivery, budget constraints… At that moment, we were looking for the fastest, shortest solution. For example, I remember deploying a simple pivot table that worked with manually entered data “for now,” instead of integrating a complex production planning algorithm. We said, “Operators can manage it, and we’ll integrate AI later.” That “later” never came for years, and every production planning cycle turned into an ordeal full of manual interventions.
With these kinds of “temporary” solutions, I accumulated debts that rolled like snowballs. I know that after a while, I spent weeks, sometimes even months, optimizing that production flow. Every shortcut taken in critical business workflows like purchasing, production, shipping, and invoicing led to unexpected disruptions and incompatibilities in other parts of the chain. A half-day’s work done at the right time turned into a months-long ordeal.
Symptoms of the Silent Killer: How Do You Recognize It?
Technical debt doesn’t show itself immediately. At first, everything seems fine, the system works. But over time, strange glitches begin to appear in the system. Adding a new feature becomes increasingly difficult, and the “if we touch this code block, everything will collapse” syndrome emerges. One day, I saw systemd units behaving strangely after a small configuration change in a customer’s system. It took days to find the root cause because there was a dependency mess created by a service added “quickly” without considering the underlying cgroup limits.
When a WAL bloat alarm went off in PostgreSQL, or when critical data was lost due to Redis’s OOM eviction policy, we were always paying the bill for those debts. I can’t forget the pain we experienced when we wrote an Nginx reverse proxy configuration to allow everything “just in case,” and then tried to integrate rate limiting and JWT/OAuth2 patterns. Every new deploy became a gamble because the underlying layers were so patched up that you couldn’t hold onto anything without it falling apart.
Living with Debt or Paying It Off? The Trade-offs
So, if it’s so bad, why does so much of it accumulate? The answer is simple: the imbalance between immediate benefit and long-term cost. When a company wants to get a product to market quickly, it’s easy to compromise on architectural discipline. The thought of “let it work now, we’ll fix it later” eases the immediate pressure. But that “later” never comes. Or when it does, the cost has grown so much that it becomes impossible to pay.
Once, on an internal banking platform, we needed to replace an old integration layer. The existing structure was the product of almost twenty years of debt. Every new integration was added with patches on top of this old structure, turning it into a layered labyrinth. Attempting to pay off that debt would mean a complete halt to the project and a rewrite lasting months. The choice at that moment was to continue living with the debt, but every new request meant getting lost in that labyrinth once again. This was one of the most painful examples of the equation: we would do X, but because of Y, we chose Z.
My Methods for Combating Debt: Pragmatic Approaches
I’ve learned a few things in fighting this debt. First, be aware of the debt. Constantly ask the team, “This is a quick fix, we’re creating technical debt, when will we pay it off?” and discuss this transparently. Second, pay it off in small steps. Instead of a large refactor project, clean up a small part of the relevant module every time a new feature is added. In my own side product’s financial calculators, whenever I add a new feature, I try to increase the test coverage of the relevant part and break down an old, poorly written method into smaller pieces.
Also, observability is crucial. Metrics, logs, traces… These are essential to see where debt is accumulating and where performance is dropping. We need the right tools and habits to understand that logs lost due to a journald rate limit were actually the last breaths of an OOM-killed service. I’ve always shared my mistakes transparently. Last month, when I switched an OOM-killed service that I had written sleep 360 for to polling-wait, I shared with the team why this created debt and how I fixed it. I wasn’t ashamed, because it was part of our learning process. I’ve seen countless times how accumulated debt creates new risks, even in security-focused tasks like tracking kernel module blacklists and constantly updating fail2ban patterns.
Final Word: No System Is Debt-Free, Unmanaged Debt Is a Killer
Technical debt is a project’s hidden cost, a killer of developer motivation, and ultimately a worm that gnaws at customer satisfaction. Ignoring it means paying much higher prices in the long run. One of the biggest lessons in my career has been to listen to the whispers of this silent killer and proactively fight it. No system is debt-free; the important thing is to manage it.
So, what was your biggest technical debt story in your career? Or what was the most effective method you used to combat this debt? I’m eagerly awaiting your comments.