What is technical debt?

Poor project management, unrealistic delivery deadlines and last-minute stakeholder requests often force team members to make short-term trade-offs that require additional work. While tech debt is sometimes a necessary trade-off to meet business needs or speed up development, excessive accumulation can slow progress, increase costs and reduce software reliability. Managing technical debt requires balancing short-term delivery goals with long-term code quality and system sustainability.

Technical debt manifests in multiple ways, from hasty workarounds to deeply embedded architectural flaws. Software engineer and author Ward Cunningham¹ introduced the concept by comparing it to financial debt, where accumulating interest over time makes it harder to pay off. Later, software development expert Martin Fowler refined the idea with his Technical Debt Quadrant², categorizing debt into 4 types:

• Reckless versus prudent: Was the debt incurred deliberately or through poor decision-making?
• Deliberate versus inadvertent: Was the team aware they were taking on debt or did it arise unintentionally?

Beyond this classification, debt takes many forms in software development. 

Architectural debt emerges when a system’s foundation lacks scalability, flexibility or maintainability. Legacy systems, monolithic architectures and tightly coupled components make updates difficult, increasing the effort required for future development.

Code debt results from rushed development, inconsistent coding practices and poor documentation. When programmers take shortcuts—such as duplicating logic, using unclear variable names or failing to follow industry standards—the amount of technical debt accumulates, making debugging and maintenance time-consuming.

Infrastructure and DevOps debt accumulates when outdated deployment processes and inefficient CI/CD pipelines hinder automation and scalability. Without proper infrastructure planning, teams might face roadblocks in integrating application programming interfaces (APIs), updating dependencies or ensuring cloud environments remain cost-effective.

Process debt stems from poor collaboration, unclear workflows and missing documentation, causing delays in feature delivery and increasing onboarding challenges. Companies that neglect agile methodologies or fail to integrate scrum principles often struggle with backlog accumulation, making it difficult to track and resolve issues efficiently.

Security debt arises when teams cut corners on encryption, authentication or vulnerability patching, leaving software exposed to cyberthreats and compliance risks. A lack of automated security testing increases the burden on teams, making it more difficult to maintain secure systems.

Technical debt, such as financial debt, accumulates interest over time. The longer it goes unaddressed, the more expensive it becomes to resolve. While taking on technical debt can accelerate time to market, failure to manage it properly results in rising maintenance costs, reduced developer efficiency and lost business opportunities.

One of the most immediate financial consequences is the increased cost of engineering hours spent on bug fixes and rework instead of new development. Teams working within a debt-heavy codebase require longer debugging cycles, making even minor changes expensive. As debt accumulates, companies must either allocate more resources to maintenance or risk delays in feature delivery—both of which increase operational costs.

Infrastructure costs also rise when outdated architectures, inefficient DevOps workflows or legacy dependencies require expensive overhauls to remain functional. Businesses may find themselves spending more on cloud storage, compute resources or third-party licensing fees simply to keep fragile systems running.

In competitive markets, excessive technical debt can slow innovation, preventing businesses from responding quickly to customer demands. Delayed product updates, recurring system failures and degraded performance can lead to customer churn, reducing revenue and damaging brand reputation. For companies in regulated industries, unaddressed security vulnerabilities can result in compliance violations, fines and legal consequences.

Managing technical debt helps enforce quality standards and communicate its impact, such as increased complexity and maintenance challenges, to CIOs and stakeholders, ensuring software stays viable and scalable over time.

Generative AI code assistants accelerate development by automating repetitive tasks and suggesting fixes, making software development more satisfying for coders. Traditional methods, such as manual testing and code reviews, are time-consuming. If used correctly, generative AI can help manage technical debt by identifying redundant code, improving readability and generating higher-quality starting code.

AI code assistants can contribute to technical debt if their outputs are accepted without proper review. AI-generated code might introduce inconsistencies or create unnecessary dependencies that later require refactoring. Human oversight ensures clear API documentation and logical function while ensuring that developers validate AI suggestions and enforce code reviews.

Managing technical debt requires balancing time to market, software quality and cost. Many companies face difficult decisions when deciding whether to release software quickly or invest more time in quality. For example, a social media engineering team might “Move fast and break things” in its early years, prioritizing rapid development over long-term maintainability. However, as technical debt accumulates, the company must shift to a more sustainable model that implements rigorous review processes to ensure quality while maintaining agility.

Governance frameworks and automation tools help organizations track and manage technical debt. Large enterprises use project management software to monitor code quality, identify bottlenecks and ensure that backlog items related to refactoring are prioritized appropriately.

Technical debt is not just a technical issue—it’s a cultural one. Companies that encourage programmers to document their code correctly, write maintainable APIs and invest in long-term software health help prevent the accumulation of bad code or legacy code.

Low-code and no-code platforms are helping organizations reduce technical debt by minimizing manual coding errors and streamlining development. 

Treating technical debt as an ongoing priority rather than a one-time fix is key to long-term sustainability. Shopify, for instance, dedicates 25% of its development cycles to addressing technical debt.

By implementing “debt sprints” within its agile workflow, the company makes certain that engineers periodically refactor and improve existing code instead of solely focusing on new features. Incorporating technical debt management into the roadmap helps teams balance feature development with necessary maintenance, ensuring that long-term software health remains a priority. A well-defined roadmap also allows project managers and stakeholders to anticipate technical debt resolution alongside new product releases, preventing last-minute trade-offs that could lead to additional issues.

Using tools to track technical debt allows teams to measure and mitigate risks proactively. Many organizations use code quality metrics and automated linting tools to prevent unnecessary complexity from accumulating within their microservices architecture. Regular analysis of the codebase helps identify areas where bad code, deprecated dependencies or inefficient structures contribute to long-term maintenance challenges. Keeping the codebase clean and modular ensures technical debt does not hinder scalability or introduce unnecessary bottlenecks in the development process.

Unrealistic deadlines can lead to rushed decisions that increase technical debt. For example, the 2013 launch of HealthCare.gov faced significant issues due to compressed timelines, resulting in system crashes, security vulnerabilities and incomplete functionality at launch. The rushed development process led to costly post-release fixes, highlighting the importance of balancing deadlines with proper software engineering practices.

By implementing comprehensive automated test suites, organizations can proactively identify and address defects early in the development lifecycle, significantly reducing the long-term burden of costly rework. This approach enables faster, more reliable software releases, ensures consistent quality and helps maintain stability across frequent updates. Continuous testing and validation, integrated into development workflows, are essential for minimizing the accumulation of technical debt and fostering a culture of quality.

Understanding the causes of technical debt helps organizations make informed decisions about whether to take on intentional debt and when to prioritize paying it down. Companies that fail to track their technical debt risk accumulating bad code, fragile systems and rising costs associated with bug fixes and infrastructure rework.