Every company that builds software – whether a scrappy startup or an enterprise giant – eventually runs into the same silent killer: technical debt. It’s the invisible weight that drags down innovation, strains developer morale, and bloats IT budgets.

In fact, by 2023, more than 90% of CTOs identified technical debt as a major obstacle, and over 70% of developers were spending at least a quarter of their time wrestling with it (Stepsize Report). Meanwhile, McKinsey estimates that it can consume up to 40% of an organization’s entire technology estate.

The debt metaphor isn’t accidental. Like financial borrowing, technical shortcuts can offer short-term gains – but they accumulate interest fast. Left unmanaged, that “interest” shows up as brittle code, unstable architecture, ballooning maintenance costs, and slower feature delivery. At worst, it derails entire projects.

This article explores what technical debt really is, why it matters more than ever, and – most importantly – how to proactively manage it before it takes a toll on your business.

Let’s dive in!

Chapters

What is Technical Debt?

Technical Debt by Definition

Technical debt is what happens when developers take shortcuts to finish a project quickly. It’s like borrowing time today and promising to fix things later.

The term was first used by Ward Cunningham in 1992 to explain how rushing code can lead to extra work down the line.

Just like borrowing money, technical debt can be helpful in the short term. It lets teams release software faster, test ideas early, or meet tight deadlines.

However, one common reason technical debt builds up is miscommunication in the workplace – when team members aren’t on the same page, decisions are rushed or misunderstood, and shortcuts pile up.

Originally, technical debt only meant messy or unfinished code. Today, it also includes things like outdated systems, old tools, and manual steps that could be automated. These issues make software harder to work with and more expensive to maintain.

Experts compare technical debt to credit card debt. A little is okay if you pay it off, but ignoring it leads to bigger problems. If teams keep adding new features without fixing old problems, the software becomes slower, harder to change, and more likely to break. But more on that in the next section!

Technical Debt vs Financial Debt

Just like borrowing money can lead to interest payments, incurring technical debt means future development will be slower and more costly.

Both technical debt and financial debt are forms of leverage. When used wisely, they can help companies grow faster than they otherwise could. Just like a business might borrow money to fund expansion, development teams might take coding shortcuts to release a product more quickly. In both cases, you’re trading immediate benefit for future cost.

Financial debt is well understood and closely managed. Companies have finance teams who track every detail – cash flow, interest rates, repayment terms, and risk levels. They rely on models, regulations, and rating agencies to ensure the debt is manageable. Lenders also assess this data carefully before approving loans, which helps keep the system accountable.

Technical debt, by contrast, is much harder to measure. It includes rushed code, skipped testing, poor architecture decisions, and manual processes that should be automated. While taking on some tech debt is often necessary, especially in fast-moving environments, most companies have no clear idea how much they actually carry – or how much is too much.

Types of Technical Debt

Technical debt comes in many forms. Some types are easy to spot – like messy code – while others hide deep in your systems. Here are the most common ones you should know about:

1. Code Debt

This happens when code is written quickly without much care. It may be messy, hard to read, or missing comments and tests. Developers might skip best practices just to meet a deadline. Over time, this makes the code harder to understand and fix. Small bugs can turn into big problems.

2. Architecture Debt

Architecture debt is when your software structure is hard to change or grow. This often comes from things like “spaghetti code,” where everything is connected in confusing ways. It slows down teams and causes bugs when one change breaks something else. It also makes moving to the cloud or scaling up much harder.

3. Infrastructure Debt

This type of debt comes from using outdated servers, tools, or deployment methods. Manual work that could be automated is another example. If your system isn’t built to support modern tools like CI/CD (continuous integration/continuous delivery), you’re falling behind – and it’s costing you time and money.

4. Data Debt

Data debt builds up when your systems store poor-quality or outdated data. Duplicate records, missing info, or bad data models make it hard to trust your numbers. When companies grow or merge, this makes combining data from different sources a nightmare. It can also cause mistakes in decision-making, especially with AI tools.

5. Testing Debt

Testing debt happens when there aren’t enough automated tests or when tests are skipped. Without good tests, it’s risky to change or add features. Bugs slip through, and teams spend more time fixing problems instead of building new things.

6. Maintainability Debt

This is when your code is too complex or full of repeated logic. It may work, but it’s not clean. That means it’s harder to update, and new features take longer to build. In the long run, it costs more to maintain software with high maintainability debt.

7. Open Source Dependency Debt

Many projects use open-source libraries to save time. But if those libraries become outdated or unsupported, they can cause big problems. Teams often forget to update them, leading to bugs, security holes, and delays.

8. AI Debt

AI systems can create their own kind of debt. If a company quickly builds AI models without solid planning, the models may become outdated, inaccurate, or even unsafe. This is called “model drift.” It’s expensive to fix later, especially if new laws or better models make old ones useless.

9. Security Debt

Security debt is the result of ignoring safety best practices. This could mean skipping password rules, using outdated software, or failing to protect private data. In AI systems, it can also mean not knowing what private info might be inside a model – which can cause serious risks.

10. Cultural Debt

Cultural debt is when the company’s habits or values stop it from improving. Maybe teams resist change or rely too much on “tribal knowledge” – things only a few people understand. This makes it harder to adopt new tools like AI or keep up with modern software practices.

Why is Reducing Technical Debt Important?

Reducing technical debt helps companies move faster, build better software, and spend less time fixing old problems. When technical debt builds up, everything slows down. Developers end up spending time on bugs and patches instead of creating new features. This can frustrate teams and delay important projects.

Too much technical debt can also lead to big failures. For example, Knight Capital lost $440 million in just 45 minutes because of a software error tied to outdated code. Barclays Bank had to pay £7.5 million in fines after a system outage left customers unable to pay bills. These events show how old, messy systems can cause real financial harm.

Bad code also hurts your product. If your app is slow, buggy, or crashes, customers will leave. That means lost sales and lower trust. Fixing these problems is harder when technical debt is high because every small update becomes risky and time-consuming.

Technical debt doesn’t just slow down software – it also slows down people. Developers don’t enjoy cleaning up messy code over and over. It leads to burnout, low morale, and even higher employee turnover. Happy teams want to work on exciting, creative problems – not patch up old ones.

High levels of debt also stop companies from being flexible. When your systems are fragile, it’s harder to adapt to new tools, customer needs, or market changes. You can’t move quickly when you’re dragging around a mountain of outdated code and systems.

The more technical debt a company has, the harder it becomes to make smart decisions, especially when using data. Poor-quality data systems can lead to wrong reports, bad predictions, and wasted opportunities. This becomes even riskier when using AI or machine learning.

In the end, reducing technical debt helps businesses:

• Move faster and compete better
• Improve software quality and reliability
• Save money and avoid costly failures
• Keep developers happy and productive
• Build trust with users and customers

Technical Debt and AI

AI developments are changing how we build software – but it’s also making technical debt more complicated.

Many teams now use AI tools to help them write code faster. Tools like GitHub Copilot or ChatGPT can quickly generate large chunks of code. This can speed up development in the short term. But there’s a catch: when developers copy-paste code without cleaning it up or checking it carefully, technical debt piles up fast.

Recent studies have shown that AI-generated code often leads to more duplicate code and fewer efforts to reuse or improve older code. This means that software becomes harder to fix and maintain over time. According to GitClear’s AI Copilot Code Quality report, the number of duplicated code blocks increased around eight-fold in 2024 compared to previous years.

While AI may boost coding speed, it can also introduce extra work. Research indicates developers spend more time debugging AI-generated code and resolving security issues. For instance, Google’s DORA report found that a 25% rise in AI usage improved code reviews, but reduced delivery stability by 7.2%.

Experts also warn that an AI-driven rush to integrate new tools could spark a technical debt “tsunami.” According to Forrester, over 50% of tech leaders expect moderate to high levels of technical debt by 2025.

Moreover, adopting AI often demands complex infrastructure changes – new data pipelines, model management systems, and governance around bias or drift. These upgrades add layers of technical debt without careful planning.

AI tools also struggle in messy or outdated codebases. If a system has tangled dependencies or lacks modular structure, AI models can’t generate useful output. Teams must first refactor and modularize their code before AI can help effectively.

But it’s not all bad news. If used wisely, AI can help reduce technical debt. For instance, AI can suggest cleaner code, generate documentation, and flag duplicates. According to CAST Software, these tools can detect code smells and promote consistent coding standards .

To make AI a helpful partner (not a debt accelerator), teams should:

• Start small with pilot projects and iterate intelligently.
• Choose stable, well-supported AI tools.
• Refactor and modularize codebase before deploying AI tools.
• Track duplicates, security risks, and delivery stability.
• Train developers in AI use and code quality best practices.

Conclusion

Reducing technical debt isn’t just about writing better code – it’s about building a stronger, faster, and more resilient business.

Author bio:

Mariela Kashukeeva is the founder of Manic SEO – an on-page SEO and manual link-building agency. With over 7-year experience in SEO and business development, she is responsible for establishing collaboration opportunities with high-authority websites and creating amazing content.

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