What Is Technical Debt? Types, Examples, and How to Reduce It

Technical debt is the future cost of choosing a quick or easy solution in software today instead of a better approach that would take longer. Like financial debt, it builds “interest” over time: the longer shortcuts stay in place, the harder and more expensive it becomes to fix bugs, add features, or keep systems secure.

Every business that relies on software has some technical debt. A little is normal and sometimes even smart. Too much slows teams down, frustrates customers, and makes every new project cost more than it should.

Below, we explain where technical debt comes from, the main types with real examples, why AI-generated code is adding to it, and how to measure and reduce it.

Where Does the Term “Technical Debt” Come From?

The term was coined by software developer Ward Cunningham in 1992. He compared shipping code quickly to taking out a loan: it helps you move faster now, but you have to pay it back later. If you don’t, the interest piles up in the form of extra work, bugs, and slower progress.

What Causes Technical Debt?

Technical debt usually comes from a mix of business pressure and technical decisions. The most common causes are:

Tight deadlines: Teams take shortcuts to hit a launch date and plan to “clean it up later.”
Changing requirements: Code written for one purpose gets stretched to do something it wasn’t designed for.
Outdated technology: Frameworks, libraries, and platforms age, and older systems become harder to support.
Missing tests and documentation: Without them, every change is risky and slow.
Quick fixes and workarounds: Patches that solve today’s problem but add complexity.
Team turnover: Knowledge leaves with people, and new developers work around code they don’t fully understand.
AI-generated code that isn’t reviewed: Code produced quickly by AI tools and accepted without proper review or testing.

Is Technical Debt Always Bad?

No. Technical debt is a trade-off, not automatically a mistake. Taking on some debt to launch a minimum viable product, test an idea, or meet an important deadline can be the right business decision. The problem is debt that goes untracked and unpaid. Like a credit card, it’s useful when managed and damaging when ignored.

The Technical Debt Quadrant

Software expert Martin Fowler created a simple way to think about how technical debt happens. His technical debt quadrant sorts it along two lines: whether the debt was taken on deliberately or by accident, and whether the decision was prudent or reckless.

	Reckless	Prudent
Deliberate	“We don’t have time to do it properly.” Shortcuts taken knowingly, with no plan to fix them.	“We need to ship now and will deal with the consequences.” A conscious trade-off with a plan to repay it.
Inadvertent	“What’s a proper design?” Debt caused by a lack of skills or standards.	“Now we know how we should have done it.” Lessons learned only after building and using the system.

Prudent and deliberate debt is usually fine. Reckless debt, whether deliberate or accidental, is the kind that causes real damage over time.

Types of Technical Debt (With Examples)

Technical debt shows up in more places than just code. Here are the most common types:

Code debt: Messy, duplicated, or overly complex code. Example: the same pricing logic copied into five places, so a price change needs five edits.
Architecture debt: A system structure that no longer fits the business. Example: an app built for 100 users that now struggles with 10,000.
Infrastructure debt: Old servers, operating systems, or hosting setups. Example: a key application still running on an unsupported server version.
Integration debt: Fragile connections between systems. Example: a nightly spreadsheet export that someone manually imports into another tool. Our guide to API integration explains the better alternative.
Testing debt: Few or no automated tests, so every release needs slow manual checks.
Documentation debt: Missing or outdated documentation, so only one person knows how something works.
Security debt: Unpatched libraries, weak access controls, or outdated encryption left in place.
Platform and configuration debt: Over-customized business platforms. Example: years of custom fields, rules, and automations in Salesforce that no one fully understands anymore.

Technical Debt in the Age of AI-Generated Code

AI coding tools have made it faster and cheaper than ever to build software. Some teams now “vibe code,” describing what they want in plain language and letting AI write most of the code. This is great for prototypes and internal tools, but it can quietly create a lot of technical debt if no one reviews what’s produced.

Common risks include:

Code that works but isn’t understood: If no one on the team knows how it works, fixing or extending it later is slow and risky.
Inconsistent patterns: Different prompts produce different styles and approaches across the same project.
Hidden security issues: Generated code may use outdated libraries or skip proper input checks.
Missing tests and documentation: Speed often comes at the cost of the safety net.

The fix isn’t to avoid AI. It’s to treat AI-generated code like any other code: review it, test it, document it, and make sure it follows the same standards as the rest of the system. Building something fast is easy now. Building something you can maintain for years still takes planning, which is why we recommend reading how to plan a successful custom software project before you start.

Signs Your Business Has Too Much Technical Debt

Small changes take weeks instead of days.
Fixing one bug regularly creates new ones.
Developers avoid touching certain parts of the system.
Only one or two people understand how key systems work.
Your software can’t connect easily to newer tools.
Security updates are delayed because they might “break something.”
A growing share of your IT budget goes to keeping things running rather than improving them.

How to Measure Technical Debt

Technical debt is hard to measure precisely, but you can track it with a mix of signals:

Code quality tools: Static analysis tools scan code for complexity, duplication, and known issues and estimate the effort needed to fix them.
Bug and incident trends: A rising number of defects or outages often points to underlying debt.
Delivery speed: If features take longer to ship over time, debt is usually part of the reason.
Maintenance vs new work: Track how much developer time goes to fixes and upkeep compared to new features.
A technical debt register: A simple, shared list of known debt items with their impact and estimated effort to fix.

How to Reduce Technical Debt

Make it visible: Keep a technical debt register so business leaders and developers see the same list.
Prioritize by business impact: Fix the debt that slows down revenue, customers, or security first, not just what bothers developers most.
Set aside regular time: Many teams reserve a fixed share of each sprint or month for paying down debt.
Refactor gradually: Improve code a piece at a time as you work on it, instead of waiting for a risky full rewrite.
Add automated tests: Tests make it safe to change code and stop old problems from coming back.
Modernize ageing systems in phases: Upgrade or replace outdated platforms step by step. A phased cloud migration strategy is often part of this.
Set standards, including for AI: Agree on code review, testing, and documentation rules, and apply them to AI-generated code too.

Reducing technical debt is often a key step in a wider digital transformation strategy, because new initiatives move faster on a clean foundation.

Frequently Asked Questions

What is technical debt in simple terms?

Technical debt is the extra work you create later by taking shortcuts in software today. It’s like borrowing time: you move faster now, but you pay it back with interest through slower changes, more bugs, and higher maintenance costs.

What is an example of technical debt?

A common example is copying the same piece of code into several places to save time. When that logic needs to change, developers must find and update every copy, which takes longer and makes mistakes more likely.

What are the main types of technical debt?

The main types are code debt, architecture debt, infrastructure debt, integration debt, testing debt, documentation debt, security debt, and platform or configuration debt.

Does AI-generated code create technical debt?

It can. AI tools write code quickly, but if that code isn’t reviewed, tested, and documented, it can add complexity, inconsistency, and security risks that are expensive to fix later.

How do you reduce technical debt?

Make debt visible with a shared register, prioritize items by business impact, set aside regular time to fix them, refactor gradually, add automated tests, and modernize outdated systems in phases.

Keep Technical Debt Under Control

Technical debt isn’t something to fear, but it is something to manage. The businesses that handle it well make it visible, decide deliberately when to take it on, and pay it down before the interest gets out of hand. That keeps their systems secure, their teams productive, and new projects on budget.

At BitWaves, we help businesses across Canada and the US assess, reduce, and prevent technical debt, from modernizing ageing applications with custom software development to cleaning up over-customized platforms through our Salesforce consulting services and keeping systems healthy with managed IT services. Get in touch to talk about the systems slowing your team down.