What Is Technical Debt?

Examples of technical debt include taking shortcuts in code, like skipping two-factor authentication to meet a deadline, and not maintaining a system, such as delaying a framework update, which can lead to security vulnerabilities or make future development more difficult. Other examples include insufficient testing, poor code quality or documentation, and using outdated libraries or poorly designed architectures.

Real world examples of technical debt include:

• Sonos: Long-deferred architectural updates, compounded by organizational instability, led to reputational damage and missed business objectives in 2024.
• Southwest Airlines: Delayed modernization of legacy systems led to catastrophic scheduling failures during a 2022 winter storm, demonstrating the cost of outdated architecture and manual workflows.
• Nokia: Years of accumulated complexity in the Symbian OS prevented rapid adaptation to smartphone trends, contributing to the company’s market collapse as iOS and Android rose.
• Knight Capital Group: Dormant legacy code inadvertently activated during a software deployment in 2012 caused $440 million in losses within 45 minutes, leading to the firm’s collapse.
• The Y2K Crisis: Widespread use of 2-digit year formats to save memory created massive remediation costs and global risk at the turn of the millennium, an example of debt embedded in foundational assumptions.

We’ll describe these real world examples in more detail, and in addition, provide general examples of the following types of technical debt:

• Skipping security features: Building a login system without two-factor authentication to ship a product faster, only to have to add it later, which is more complex since the initial design didn’t account for it. 
• Outdated libraries and frameworks: Using an older version of a framework that contains security risks. Updating it would require extensive code changes, so the update is postponed, accumulating debt. 
• Insufficient or missing test coverage: Forgoing automated testing or writing incomplete tests to save time, which results in more bugs and higher maintenance costs in the future. 
• Poor code quality and duplication: Writing code that is hard to read, maintain, or modify due to poor design choices or lack of standards, making it difficult for new developers to work with. 
• Lack of documentation or outdated documentation: Not documenting code or project architecture, which leads to confusion and makes it time-consuming for team members to understand and work with the system later. 
• Hard-coded values and fragile implementations: Writing values directly into the code instead of making them configurable, which prevents the application from being flexible and requires code changes for simple adjustments.
• Suboptimal initial architecture decisions: Choosing architectures that don’t support future scale or flexibility creates long-term constraints. These early decisions often trade simplicity for speed.
• Tightly coupled components that block change: Highly interdependent modules make it hard to isolate and update functionality. This tight coupling increases the risk of system-wide failures and slows down development.
• Accumulated bug backlogs: Postponing bug fixes in favor of new features results in a growing list of known issues. These unresolved defects erode software quality, frustrate users, and divert engineering time away from innovation.

Over time, technical debt can impede productivity, increase the likelihood of defects, and make system upgrades and scaling efforts significantly more challenging. While some technical debt is strategic and temporary, taken on knowingly to achieve business goals, unchecked or unmanaged technical debt can rapidly constrain an organization’s ability to innovate and respond to market demands.

Real-World Examples of Technical Debt

Note: The examples in this section are based on publicly available information.

1. Sonos

Sonos’s 2024 app overhaul became a case study in the risks of long-ignored technical debt. For over two decades, the company built new features on top of aging infrastructure, postponing a deeper rebuild. When the time came to support a new product line like the Sonos Ace headphones, the existing codebase proved too brittle and outdated to handle evolving requirements. The decision was made to rebuild the app, during a period of aggressive product timelines, company reorganization, and layoffs that included key QA staff.

The result was a chaotic launch marked by customer outrage, internal conflict, and a surge of over 30,000 complaint emails to the CEO. According to an internal review, teams focused more on patching legacy issues than delivering new functionality, and the app shipped with bugs customers deemed critical. 

2. Southwest Airlines

In December 2022, Southwest Airlines experienced operational chaos when its antiquated crew scheduling systems failed to cope with weather disruptions and a surge in flight rescheduling. Their systems, built with outdated architectures and manual dependencies, struggled to recover, resulting in thousands of canceled flights and widespread customer dissatisfaction. The root cause pointed directly to accumulated technical debt in the airline’s IT infrastructure.

Southwest had postponed critical investments in modernizing its technology stack, prioritizing short-term cost savings over architectural upgrades. When these fragile systems broke under stress, the resulting fallout cost the company hundreds of millions in lost revenue and brand value. 

3. Nokia

Nokia’s decline in the smartphone market, roughly corresponding to the period 2006 to 2013, is partly attributed to technical debt accumulated from its Symbian operating system. For years, Nokia’s software stack grew increasingly complex and convoluted, making rapid development and innovation difficult. When iOS and Android emerged, Nokia struggled to adapt its aging codebase, which was not designed for modern touch interfaces or app ecosystems. 

The resulting inability to innovate quickly led to a significant loss of market share. Nokia’s story illustrates how technical debt can constrain an organization’s capacity to respond to market shifts. Legacy architecture and failure to modernize left the company lagging behind more agile competitors despite its early dominance. 

4. Knight Capital Group Trading Disaster

In August 2012, Knight Capital Group suffered a trading disaster stemming from technical debt related to obsolete and poorly maintained code. A new automated trading software deployment unintentionally reactivated dormant, outdated code paths, resulting in a flurry of unintended trades worth billions of dollars. Within 45 minutes, the company lost $440 million, ultimately leading to its acquisition and dissolution as an independent entity.

The incident revealed how unresolved technical debt and inadequate change management can have catastrophic financial and reputational consequences. When old, undocumented, and untested code is left in production environments, the risk of such failures multiplies.

5. The Y2K Crisis

The Y2K crisis is a textbook case of technical debt from shortsighted system design. For decades, software developers stored years as two digits to save memory and storage, assuming their systems wouldn’t run past 1999. This shortcut plagued thousands of legacy applications and critical infrastructure when the calendar rolled over to the year 2000. 

Remediation required extensive code audits and updates across industries, costing billions of dollars globally and highlighting the impact of deferred technical decisions on operational continuity. The effort necessary to resolve Y2K issues diverted significant resources from innovation and other business priorities.

Technical Debt Examples by Category 

Let’s review the main types of technical debt and see hypothetical examples of each one of them.

1. Skipping Security Features

Omitting essential security features like two-factor authentication, role-based access control, or input validation is a common form of technical debt, especially during early development phases. Teams may bypass these controls to accelerate product delivery, assuming they can address security later. 

However, retrofitting security into an existing system is often complex and risky, particularly if the original design didn’t account for secure workflows, threat models, or regulatory requirements. This form of debt accumulates silently but becomes critical as user data, application scope, and compliance obligations grow. Missing or weak security controls expose systems to vulnerabilities and increase the likelihood of breaches.

2. Outdated Libraries and Deprecated Dependencies

Relying on outdated libraries, legacy code, or deprecated dependencies introduces technical debt by exposing systems to security vulnerabilities and missing performance or feature improvements present in newer releases. As well-supported libraries reach end-of-life, they no longer receive patches or updates, creating systemic risks that are often hidden until a critical vulnerability becomes public knowledge. 

Additionally, once dependencies fall too far behind current versions, the effort required to upgrade increases and in some cases, a complete system rewrite might be necessary. Frequent dependency updates ensure software remains secure, stable, and compatible with modern tooling. 

Neglecting this practice causes small incompatibilities to snowball into larger integration challenges, causing projects to stall when more urgent fixes or updates are needed. Outdated dependencies can also make it harder to recruit and retain developers familiar with obsolete technologies, adding organizational risk and compounding technical debt over time.

3. Insufficient or Missing Test Coverage

Inadequate or absent automated testing is a prime source of technical debt. Without sufficient unit, integration, and end-to-end tests, teams lack confidence that changes won’t break existing functionality. As a result, code changes require extensive manual testing or, worse, get deployed without comprehensive verification, increasing the likelihood of production failures. 

Poor test coverage is often an early shortcut to meet deadlines, but its negative impact surfaces quickly as the complexity of the codebase grows. Proper test coverage acts as a safety net against regressions and is crucial for enabling refactoring and rapid delivery. 

Accumulating features without complementary tests means future enhancements or bug fixes may inadvertently cause errors elsewhere. Over time, the absence of meaningful tests increases the cost of each change, slows release cycles, and creates a brittle system that is resistant to innovation and improvement.

4. Poor Code Quality and Duplication

Duplicating code, copying and pasting logic across multiple parts of a system, creates a maintenance burden and is a classic form of technical debt. Every duplicate must be updated individually when changes are needed, raising the likelihood of introducing inconsistencies or missing a spot. 

Code duplication usually occurs when taking shortcuts to meet tight deadlines or when modularity and reuse are not prioritized in the development process. As duplicated logic spreads across a codebase, tracking down and fixing bugs becomes increasingly complex. 

5. Lack of Documentation or Outdated Documentation

Insufficient or outdated documentation contributes to technical debt by making it difficult for new team members to understand and safely modify a codebase. When critical architectural decisions, APIs, or integration points are only communicated verbally or locked in tribal knowledge, teams lose efficiency. The absence of clear documentation increases onboarding time, introduces inconsistencies, and often results in duplicated work or subtle, easily avoidable bugs.

Outdated documentation is equally problematic. When documentation falls behind the system’s current state, developers can be misled, resulting in mistakes and wasted time. Continuous documentation updates aligned with code changes are crucial to mitigate this form of debt. 

6. Hard-Coded Values and Fragile Implementations

Hard-coding configuration values directly into code, such as environment variables, credentials, or API endpoints, introduces significant technical debt. These shortcuts are often taken for rapid prototyping or testing but create landmines in production environments, as changes require code modifications and redeployment. Hard-coded values undermine flexibility  and expose systems to errors or security lapses if not managed properly. 

Fragile implementations that are tightly bound to specific behaviors or assumptions can break easily as requirements evolve. Systems designed without flexibility, such as not abstracting configuration or failing to anticipate scaling needs, create bottlenecks that are expensive to fix later.