What Is Technical Debt? A Plain-English Guide for Engineering Teams

In this article:

• What is technical debt?
• Where the term comes from: Ward Cunningham
• What causes technical debt?
• What are the consequences?
• How to recognize it in your codebase
• Conclusion

What is technical debt?

Technical debt is the accumulated cost of shortcuts, outdated decisions and deferred improvements in a software system. Like financial debt, it accrues interest: the longer you leave it unaddressed, the more expensive it becomes to fix. Understanding what is technical debt is the first step any engineering team must take before deciding how to act on it.

The term captures a simple reality: every time a team ships code that is “good enough for now” rather than properly structured, they are borrowing future engineering time. That borrowed time will eventually need to be repaid, usually at a higher rate because the codebase has grown around the original shortcut.

Technical debt is not inherently bad. A deliberate trade-off made with full awareness, documented and scheduled for remediation, is a valid business decision. The problem is when debt accumulates silently, without measurement or a repayment plan, until it slows delivery to a crawl.

For engineering leaders, the practical question is not whether technical debt exists in your system (it does, in every system), but how much you have, whether it is growing, and whether it is blocking the outcomes your business depends on.

Where the term comes from: Ward Cunningham

The phrase “technical debt” was coined by Ward Cunningham in 1992. Cunningham used the financial analogy to explain to non-technical stakeholders why shipping quick code creates future costs. His original framing was specific: debt is acceptable when you understand the trade-off and plan to address it. Reckless or unconscious shortcuts are something else entirely.

Martin Fowler later expanded the idea with the Technical Debt Quadrant, which distinguishes between deliberate and inadvertent debt, and between reckless and prudent decisions. This framework, covered in detail in our article on technical debt types, gives engineering teams a useful vocabulary for categorizing the debt they find.

The financial analogy has limits. Unlike a loan, technical debt does not have a fixed interest rate or a repayment schedule. It compounds in non-linear ways: a single poorly designed module that gets depended upon by twenty others multiplies the remediation cost by a factor that is hard to predict. That unpredictability is precisely why measurement matters.

What causes technical debt?

Technical debt has several distinct causes. Recognizing the root cause in your specific situation determines what kind of remediation makes sense.

Time pressure and deadline-driven shortcuts. The most common cause. A team ships a feature under pressure using an approach they know is suboptimal. If the shortcut is documented and the team returns to it, this is manageable. If it is neither documented nor revisited, it compounds quietly.

Outdated technology choices. A library, framework or language that was the right choice three years ago may now be unmaintained, incompatible with modern tooling, or simply not understood by anyone currently on the team. One company we worked with was running a billing system on a framework whose last security patch was in 2018.

Lack of shared standards. When a team grows quickly without establishing coding conventions, each developer solves the same problem in a different way. The result is inconsistency that makes every change harder than it should be.

Inadequate testing. Code without tests cannot be safely changed. Teams that skip tests to move faster discover that every subsequent change requires manual verification and carries high risk, which slows them down more than the tests ever would have.

Accumulated feature complexity. Features added without refactoring existing structure create tangled dependencies. Over time, what started as a clean architecture becomes a system where changing one thing breaks something apparently unrelated.

What are the consequences?

The consequences of unmanaged technical debt technical debt are predictable and measurable. Engineering teams working inside heavily indebted codebases consistently report the same patterns.

Slower delivery. What should take a day takes a week, because every change requires understanding a web of undocumented dependencies. At one client we supported, deployment frequency was once every six weeks, driven entirely by fear of regressions. After addressing the structural debt, they reached 70% faster deployment cycles.

Increasing incident rates. Fragile code breaks more often. Teams spend a growing fraction of their sprint capacity on unplanned work. The same client saw 40 production incidents per month before remediation, dropping to 4 afterward.

Developer attrition. Engineers do not enjoy working in codebases where they feel unable to make progress. Experienced developers leave. Replacements take longer to onboard because the system is hard to understand.

Blocked business initiatives. New product features require changes to core modules that no one wants to touch. Technical debt stops being a “technical” problem and becomes a product strategy constraint.

Acquisition risk. Buyers and investors conducting software due diligence increasingly flag technical debt as a material risk. See our software due diligence service for what this process involves.

How to recognize it in your codebase

You do not need a specialized tool to spot early signals of technical debt. The following patterns indicate a codebase that warrants closer inspection.

Code that no one wants to touch. If a module consistently generates comments like “I would not change this without thorough testing” or “I am not sure what this does but do not remove it,” that is debt.

Changes that take far longer than they should. If adding a new field to a form requires modifying eight files, the architecture is not supporting the business anymore.

Frequent regressions. If fixes in one area regularly break something else, coupling is too tight and test coverage is too thin.

Long onboarding times. If new engineers take more than two weeks to make their first meaningful contribution, the codebase is not legible.

No visibility into health metrics. If you cannot answer “what is our code coverage, average cyclomatic complexity and duplication ratio,” you cannot make informed decisions about where to invest remediation effort.

Conclusion

Technical debt is a normal part of building software under real-world constraints. The danger lies not in its existence but in its invisibility. Teams that measure it, categorize it and build explicit remediation into their planning cadence keep it manageable. Teams that ignore it eventually find it managing them.

If your system shows the warning signs above, the next step is a structured assessment, not a rewrite. Eden Technologies has helped over 200 companies understand and reduce their technical debt without disrupting live systems. Our tech debt solution page outlines how that process works.

Does your codebase have these problems? Let’s talk about your system