Technical Debt: The Hidden Cost Of Shipping Fast And Thinking Later

Ever shipped a tiny change and somehow broken six unrelated features you didn’t even touch? That, dear friends, is tech debt doing what it does best: lurking in the corners of the codebase you refused to refactor. You know exactly what I mean; those files stuffed with spaghetti logic, the ancient functions no one fully understands, that one part of the product that "just works" (but no one knows how - the code is unreadable). Have you ever spent hours tracing a bug, only to realize the real issue was a shortcut someone took nine months ago under deadline pressure? You’re not alone. Most of us have wrestled with tech debt left behind by old teammates, past versions of ourselves, or a product roadmap that prized speed over sustainability. Tech debt is the compounding interest of rushed decisions, and today, we’re unpacking what it is, why it doesn’t totally suck, and why it’s way more dangerous than most engineers realize.

What Is Technical Debt, really?

Technical debt is the cost of choosing speed over long-term code quality. It happens when teams ship fast by making subpar technical decisions, knowing their process isn’t ideal. In the short term, this helps hit deadlines and prove value. But over time, the code becomes harder to understand, slower to change, and more expensive to maintain, just like financial debt accrues interest if you don’t pay it back.

Ward Cunningham, who coined the term technical debt back in 1992, said:

Shipping first-time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite. The danger occurs when the debt is not repaid. Every minute spent on code that is not quite right for the programming task of the moment counts as interest on that debt.

In plain English, when you create tech debt in your codebase, you're buying time to ship features faster. This quick-and-dirty code is fine if you revisit and fix it soon. When you don't, however, problems start to pile up. The longer you leave that messy code in place, the more time and effort you waste working around it.

Not all tech debt is created equal. Sometimes, you create intentional debt. This is the kind you take on knowingly - sprinting through features on an MVP checklist, building a demo overnight, or hacking together a prototype for a pitch. You know the code isn't great, but you accept the tradeoff to move fast, planning (hopefully) to clean it up later. Accidental debt, on the other hand, creeps in when no one's looking. It's what happens when specs change mid-sprint, when junior devs copy outdated patterns for core modules, or when your app is still running on a framework the internet forgot. One is strategic. The other is the programming equivalent of finding mold behind your wall.

Picture this: your team needs to ship a new onboarding flow by Friday. The designer mocks up a slick multi-step signup form, and the backend team realizes they’ll need to touch one ancient login module, written four years ago by someone who’s since moved to Canada and started a farm.

Ideally, this is the moment to refactor the login logic: separate concerns, clean up those 300-line functions, maybe even add some tests. But there’s no time. So instead, someone hacks in a workaround. It works for the deadline. But every new feature added to auth starts to break something else: session handling gets messy, error states become unpredictable, and adding social login becomes a full-blown crisis.

Months later, that one decision to “just patch it” means every auth-related ticket takes three times longer, and new engineers onboard with a warning: “DON'T TOUCH THE LOGIN SERVICE!” That’s tech debt in a production codebase in today's world.

How Tech Debt Happens

You don't just owe a bank 4x the amount you borrowed right off the bat (at least, I'd like to think so). You end up heavily in debt when you allow loans to rack up interest without paying them off in time. Similarly, in software production, tech debt slowly rises, disguised as "just one more quick fix" or a // TODO: clean this up comment. Over time, these small decisions start compounding, until you’re debugging a system that feels more like an archaeological excursion than a codebase. Here’s how it usually happens:

• Ship-or-die deadlines: You had five days to do three weeks of work, so you cut corners to ship on time. The shortcut worked. The code stayed. No one circled back like they said they would.

• Missing docs and tests: What starts as “we’ll document this later” quickly becomes a game of telephone, where future devs are forced to reverse-engineer your decisions from vague commit messages and test cases that haven't been updated in months.

• Team churn and knowledge silos: The one engineer who understood how the invoicing system worked just rage-quit and joined a fintech startup. Now you’re stuck with brittle logic, zero documentation, and multiple Slack threads that end in “nevermind, fixed it” and no explanation on what the code does.

• Partial migrations: Half your service uses Paystack, the other half uses Flutterwave, and now someone’s suggesting “let's try Monnify” for virtual accounts. Meanwhile, your original payment logic is still tangled up in custom webhook handlers from 2020. No tests, no docs, and no one brave enough to refactor it during working hours.

• Copy-paste cargo culting: You found a working snippet on StackOverflow and dropped it in. It solved your problem, but now no one knows what it actually does. Worse, it’s breaking edge cases that nobody tested for.

• Technical or Requirements Constraints: Not all tech debt is born from poor decisions. Sometimes, it’s the result of working within immovable limits. Maybe you’re forced to use an outdated API because the vendor hasn’t released v2 yet. Maybe the infrastructure team won’t approve a new database engine, so you’re stuck bending your current one into unnatural shapes. Or maybe the product spec just changed again mid-sprint, and your only option was to duct-tape the new logic onto whatever already existed.

  The Many Faces of Tech Debt

Not all debt is code and not all of it is your fault. Tech debt can show up in different layers of your system:

Tech debt isn’t just messy code. It’s every inefficiency your team pays for, over and over again.

Spotting Tech Debt

Tech debt isn't always apparent. Unlike loan apps threatening your entire lineage if you don't pay up, tech debt often hides in plain sight, quietly draining your team’s velocity and morale until you realize you’ve spent three days fixing a bug that should’ve taken twenty minutes. You just had to fix one more thing, one more broken module that the original one was dependent on, until you ended up rewriting the entire feature, and you missed the deadline. So how do you know when your codebase is quietly screaming for help?

• Quantitative Red Flags

  • Pull requests that drag on forever: If simple changes start taking days (or weeks) to finish, chances are you're wrestling with hidden messes under the hood.
  • Code that’s too complex to follow: When a single function has so many “if this, then that” cases, it feels like reading a legal contract. That’s a sign the logic has gotten out of hand.
  • Barely any tests: If less than half your code is covered by automated tests, you're flying without a safety net. Every new change becomes a gamble.

• Qualitative Smells

  • “Don’t touch that part of the system.” If your team avoids certain files because they always break something, you’ve got serious baggage there.
  • TODO comments from the BC Era: Notes left behind like “fix this later” that were never touched again, usually by people who’ve already left the company.
  • Code reviews full of confusion: When teammates constantly ask, “What does this even do?” or “Why was it written this way?”, you’re probably looking at bad code that needs an update.

• Tools That Help

  • Code scanners (like SonarQube or DeepSource) that point out messy areas automatically.
  • Health scores that rank your files based on how hard they are to maintain.
  • Decision logs that explain why something weird was built that way (instead of guessing).
  • Incident history that shows which parts of your system are always breaking at the worst times.

Real-World Impact of Technical Debt on Software Systems

Tech debt isn’t just an “engineering problem.” It’s a business problem. Left unmanaged, it silently bleeds time, money, morale, and momentum across the entire organization. The features get slower to build. The bugs get harder to trace. The outages get more frequent. And the developers? They start burning out. Here’s what that looks like in real life:

• Slower Development: New features start taking forever, not because your team isn’t skilled, but because every change means wading through a minefield of brittle, undocumented code. You want to add a “forgot password” feature, but it turns into a three-day saga because it touches legacy auth logic that hasn’t been touched since 2019.
• More Outages: Remember that one cron job nobody understood but everyone hoped would keep working? It didn’t. Tech debt creates fragile systems that break at the worst times, usually on weekends, during a promo launch, or when your lead dev is on vacation. Suddenly, your team is spending more time fixing fires than actually shipping features.
• Higher Costs: Every patch, workaround, and unexplained bug adds up. Engineers burn more hours. Customers encounter more issues. Infra teams over-provision to compensate for bloated, inefficient logic. That’s real money leaking out of the product, disguised as "normal delays."
• Team Burnout: No one enjoys working on a broken foundation. When your smartest engineers are spending half their time fixing the same parts of the system over and over, they’ll either check out or check LinkedIn. Tech debt is the silent morale killer that makes even small wins feel exhausting.
• Business Blockers: Sometimes, the tech debt is so bad you simply can’t build what the business wants. “We can’t add multi-currency support yet” or “That will take three months instead of two weeks” becomes a regular thing in product planning. And no matter how strong the roadmap looks, delivery suffers.

Strategies to manage and repay tech debt

Tech debt isn’t the root of all evil. But like any loan, you have to track it, manage it, and eventually pay it down. Here’s how to do that while still staying on top of your roadmap.

• Start a Debt Register: You can’t fix what you don’t track. Maintain a running list of known debt across the codebase - what it is, where it lives, how often it causes problems, and how painful those problems are. Think of it like a credit card statement, but for your repo. High-interest debt (frequent bugs, blockers) should always rise to the top.
• Follow the Boy Scout Rule: Whenever you touch a file, refactor a little. Rename variables. Break down bloated functions. Add a comment. Leave it cleaner than you found it. These micro-improvements compound over time and help shift the codebase from survival mode to sustainable.
• Refactor Within Features: Don't schedule refactoring for "someday." When working on a feature that touches janky code, bake cleanup into the story. That way, you get new functionality and better maintainability in one go, and no one has to justify a separate “tech debt ticket” that product will ignore.
• Schedule Dedicated Debt Time: Set aside regular time to tackle known issues: maybe it’s “Clean-Up Fridays” or a full debt sprint every quarter. You’ll never eliminate debt entirely, but routine pay-down keeps it from spiraling into a crisis. Treat it like maintenance on your house. You can either patch the leak now or replace the roof later.
• Automate Testing & CI Gates: The best way to prevent new tech debt? Don’t let it merge. Use automated tests, linters, and CI rules to catch bad patterns early. Every broken test or failed check is a helpful "nope" from your future self.
• Document Your Shortcuts: If you take a shortcut, explain why. Use docs, inline comments, or ADRs (Architecture Decision Records) to give future devs context. "This is janky, but we did it to hit the Q2 deadline, and here’s what we’d like to change when we revisit it." Now your tech debt has a paper trail and a plan.
• Have Kill-Switch Courage: Sometimes the most efficient refactor is a delete key. If a module is causing more pain than value, and no one knows why it still exists, just rip it out and rewrite it. Legacy systems, abandoned features, or duplicate services deserve a graceful exit, not eternal babysitting.

When It’s Actually Worth It

Not all tech debt is bad. Sometimes, it's the smartest move. Early in most software engineering projects (when you're testing ideas, racing toward product-market fit, or building a one-off demo), speed matters more than spotless architecture. Taking on debt intentionally can help you move fast, learn quickly, and prove value. The key is doing it with your eyes open: track it, cap it, and set a date to pay it back. Debt becomes dangerous not when you borrow, but when you forget you ever did.

References

• Towards an Ontology of Terms on Technical Debt
• Cunningham, W. (1992). The WyCash Portfolio Management System. OOPSLA '92 Experience Report

Have you dealt with painful tech debt before? Maybe you’ve left behind a shortcut you wish you hadn’t, or inherited code that made no sense. Share your stories, your worst TODOs, or that one file everyone avoids. Let’s talk about it!