5 Real-World Technical Debt Examples (And Why AI is the Newest One)

Key Takeaways

• Debt is Borrowed Time: Technical debt isn't just "bad code"—it's a loan you take out against future velocity. If you don't pay the principal, the interest (maintenance cost) will eventually consume your entire sprint capacity.
• The "Infinite Intern" Risk: AI tools like Copilot are powerful but dangerous. They often generate verbose, context-free code that passes tests but is impossible to maintain. This is the newest, fastest-growing form of debt.
• Cognitive Debt Matters: It's not just about code structure. "Ghost Documentation" (docs that lie) forces developers to reverse-engineer reality, doubling the cost of every bug fix.
• Pay It Down Strategically: Don't just "fix bugs." Allocate 20% of every sprint to refactoring, audit your dependencies to kill bloat, and stop merging AI code you can't explain.

In the world of software development, speed is often king. But the relentless pressure to deliver features creates a silent saboteur working against your long-term success: technical debt.

Coined by developer Ward Cunningham, the term perfectly captures the trade-off: taking a shortcut now to meet a deadline is like taking out a loan. You get the immediate benefit, but you'll have to pay it back later, with interest. This interest manifests as slower development, increased bugs, and frustrated engineers.

Left unmanaged, this "code debt" can cripple a project.

But let's move beyond the abstract definition. We all know what "Legacy Code" feels like. But have you learned to spot the new wave of "AI Debt" yet? It looks perfect on the surface, but it rots faster than anything you've written manually.

Here are 5 concrete examples of critical technical debt you might be ignoring right now, along with a playbook for fixing them.

Example 1: The Monolithic Monster (Architectural Debt)

This is the classic example of design debt. A monolithic architecture, where all components of an application are intertwined into a single, massive codebase, eventually becomes a significant bottleneck.

A slight change in one part of the system can have unforeseen consequences elsewhere, making updates risky and time-consuming. This tight coupling makes it nearly impossible to scale, update, or replace individual components independently.

The "interest" is paid whenever a simple feature request requires a full-system deployment, or when a bug in a minor feature brings down the entire application.

Example 2: Dependency Hell (The Silent Killer)

Your software relies on a web of third-party libraries and frameworks—your dependencies. Neglecting to update these is like refusing to fix a leaky roof. Initially, it seems harmless.

Over time, however, these outdated dependencies become major security vulnerabilities. Furthermore, the longer you wait, the larger the gap between versions becomes, making the eventual upgrade a massive, high-risk project.

This debt forces development teams to choose between running insecure code and halting all feature development for a painful, lengthy upgrade cycle.

Example 3: The "Infinite Intern" Bloat (AI-Generated Debt)

This is the newest and most dangerous form of debt.

It happens when Junior developers use AI to generate 50 lines of code where 5 would do. The code passes unit tests. It looks syntactically perfect. But it is verbose, hard to read, and lacks context.

Because AI optimizes for "probable" answers rather than "elegant" ones, it often introduces unnecessary complexity or hallucinates helper functions you don't need. I call this the "Infinite Intern" problem. You are merging code that no human fully understands, which means no human can safely refactor it later.

Recent research suggests a worrisome trend: AI-generated code often exhibits excessive verbosity, contributing to technical debt and complicating maintenance tasks. This accumulation of tech debt is the silent tax of uncritical AI adoption.

[Read more: Why Senior Devs Must Become AI Editors]

Example 4: Ghost Documentation (Cognitive Debt)

Technical debt isn't just in the code; it’s also in the lack of knowledge surrounding it.

When documentation says one thing but the code does another, you have "Ghost Documentation." The "why" behind design decisions is lost, and onboarding new team members becomes an exercise in frustration and reverse-engineering.

This forces developers to spend valuable time deciphering legacy code instead of building new features. It creates knowledge silos and increases the "bus factor"—the risk associated with key developers leaving the team.

Example 5: The Fragile Foundation (Test Debt)

A lack of comprehensive automated testing is one of the most dangerous forms of technical debt. Without a robust suite of unit, integration, and end-to-end tests, developers cannot refactor or add new code with confidence. Every change carries the risk of introducing a regression.

I once worked on a payment system where the "test suite" was just one guy named Dave manually clicking buttons in the staging environment. When Dave went on vacation, we shipped a bug that cost us $50k in lost transactions. That is the cost of Test Debt.

This fear leads to development paralysis, where the team is afraid to touch the existing codebase. Accumulated bug debt—a backlog of known issues that are never prioritized—compounds this problem, eroding user trust and undermining Code Quality standards.

Actionable Strategies to Systematically Eliminate Debt

Paying down technical debt requires a conscious, strategic effort. It’s not something that will fix itself. Here are five actionable strategies that correspond directly to the examples above.

1. Stop Generating Code You Don't Understand

If you can't explain the logic behind an AI-generated function, you are not allowed to commit it. Treat every AI suggestion as a "Draft," never as the "Final Copy."

2. Strategic Refactoring (Debt Sprints)

For monolithic monsters and codebase chaos, refactoring is the primary tool. This isn't a vague "clean up the code" task. It should be a strategic initiative. Schedule dedicated time for refactoring—often called "debt sprints"—to address specific issues like removing duplication or externalizing hard-coded values.

3. Proactive Dependency Management

To combat outdated libraries, establish a proactive management policy. This requires strong Technical Leadership to enforce. Use tools like Dependabot or Snyk to automatically scan for outdated and vulnerable dependencies. Institute a "small and frequent" upgrade policy. It is far less risky to apply a series of minor version updates than one massive major version jump.

4. Cultivating a Documentation-First Mindset

Treat documentation as a first-class citizen of your software, not an optional extra. Adopt a "documentation-as-code" approach, where documentation lives in the repository alongside the code and is updated as part of the pull request process.

5. Elevating Test Automation

Paying down test debt requires investing in automation. Prioritize building out your automated testing suite, focusing first on the most critical and complex parts of your application. Integrating automated testing into your DevOps pipeline ensures that no new code is merged without adequate coverage, preventing new debt from accumulating.

Conclusion: Reclaiming Agility

Technical debt is an unavoidable reality in software development. However, it is not a technical problem; it is a business problem with a technical solution. Allowing it to accumulate unchecked is a recipe for stagnation, security risks, and developer burnout.

The path forward is not about writing "perfect" code. It's about making conscious, informed decisions. It’s about treating your codebase as a long-term asset, not a short-term deliverable.

Don't let the AI robot borrow money in your name without checking the interest rate. By embedding quality into your culture and strictly auditing AI output, you can shift from firefighting to building.