What is Technical Debt?

Technical debt is one of those terms that seem to mean everything and nothing at the same time. Originally coined by Ward Cunningham (one of the authors of the Agile Manifesto) as a metaphor, it was designed to help explain to non-technical stakeholders — like finance teams — why it’s important to invest time in improving or refactoring code. (For more on balancing these priorities, see the article on Engineering vs. Product: The Tug-of-War for Priorities).

The concept compares cutting corners in software development to borrowing money: it provides immediate value but comes with the cost of “interest” over time. The more technical debt you accrue, the higher the “interest payments” in the form of reduced maintainability, slower development, and increased risk of bugs.

At its core, technical debt is the idea of implementing a quick solution that isn’t necessarily the best or most sustainable. While this can sometimes be a conscious and strategic choice (e.g., to meet a critical deadline), it can also result from oversight or lack of experience.

The Cost of Technical Debt

Time Cost

A 2018 study by Stripe revealed that developers spend approximately 42% of their time dealing with technical debt (fixing bad code, debugging, refactoring, and modifying existing systems). This means nearly half of their productivity is lost to maintaining past decisions instead of building for the future.

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Delivery Time

Companies that actively address technical debt ship products 51% faster on average than those that ignore it. Technical debt doesn’t just slow developers, it delays innovation, leaving businesses at a competitive disadvantage.

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Talent and Retention

Developers dislike working with messy, overly complex codebases because it makes their work stressful and unfulfilling. Instead of focusing on innovation, they spend their time fixing issues and deciphering tangled code. A study found that developers working in areas of the code with high complexity are 10 times more likely to quit than those working in well-maintained systems.

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The Hidden Costs Add Up

Technical debt impacts businesses on multiple levels:

• Productivity: Time spent fixing problems means less time creating value and driving growth.

• Time-to-Market: Projects with significant technical debt take twice as long to deliver, putting companies at risk of falling behind competitors.

• Talent: A disorganized codebase leads to unhappy developers, higher stress, and ultimately higher turnover — making talent retention a costly challenge.

Addressing technical debt isn’t just about cleaning up code; it’s about protecting your company’s ability to innovate, compete, and retain top talent in the long run.

Example: Fibonacci Sequence

To illustrate the concept of technical debt, let’s consider a scenario where the only objective is to code as quickly as possible. In this case, the primary criterion for success is the time it takes to type the solution, let’s say it takes 1 day to type each character. We’ll look at three different implementations of the Fibonacci sequence and how this “shortcut mindset” can lead to technical debt.

1. Best Implementation

def fibonacci(n: int) -> int:
    """
    Calculate the nth Fibonacci number.
    
    Args:
        n (int): The position in the Fibonacci sequence (0-based).
    
    Returns:
        int: The nth Fibonacci number.
    """
    if n < 0:
        raise ValueError("Input must be a non-negative integer.")
    a, b = 0, 1
    for _ in range(n):
        a, b = b, a + b
    return a

Typing time: 365 days

Key Features:

• Descriptive variable names, clear comments, and proper error handling

• Scalable and adaptable for future requirements

• Handles edge cases and ensures maintainability

Real-Life Analogy:

This mirrors a well-structured development process:

• Planning: The team defines the problem, designs the architecture, and anticipates edge cases.

• Development: Developers write clear, scalable, and maintainable code using best practices.

• Documentation: Comprehensive inline comments and external docs ensure future developers understand the code.

• Testing: The code undergoes rigorous testing, including unit, edge case, and integration tests.

The result is high-quality software that’s easy to maintain and future-proof, though slower to build initially.

2. Short and functional implementation

f = lambda n, a=0, b=1: n and f(n-1, b, a+b) or a

Typing time: 49 days

Key Features:

• Compact and functional, but lacks comments and explicit error handling

• Prioritizes speed over readability and maintainability

Real-Life Analogy

This reflects a results-driven development approach, focused on speed and functionality over clarity:

• Planning: Minimal or skipped entirely. The focus is on a working solution.

• Development: Developers implement a fast, working solution with limited readability.

• Documentation: Minimal or missing. Developers are expected to “figure it out.”

• Testing: Basic testing with little attention to edge cases or future scalability.

Challenges:

Adding features like input validation or optimizations requires significant rewrites. While manageable for small projects or prototypes, this approach becomes problematic as complexity increases.

3. Minimal typing

For an extreme example, let’s use the Jelly programming language, a language designed for the shortest possible code. It’s used in competitions like code golf. Here’s how you calculate the Fibonacci sequence:

+¡1

(If you want to try it here’s a link)

Typing time: 3 days.

Key Features:

• Extremely short, but completely unreadable for most developers

• No error handling, documentation, or scalability

Real-Life Analogy

This approach mirrors a hackathon / MVP mindset, where speed is everything and readability doesn’t matter:

• Planning: None. Just code whatever works.

• Development: The shortest solution possible, even if no one understands it.

• Documentation: Completely absent.

• Testing: Minimal testing — if it works once, that’s good enough.

Challenges:

• Modifications are nearly impossible.

• It’s often easier to rewrite from scratch than build on this foundation.

The Case for Technical Debt

Why? Because perfect code is rarely the goal. Over-engineering every solution to anticipate all future needs can waste valuable time that could be spent delivering value to users. Strategic technical debt allows you to move quickly, meet deadlines, and validate ideas.

However, the real problem arises when short-term speed gains create long-term bottlenecks. Left unchecked, technical debt accumulates like unpaid interest on a loan. Instead of enabling fast development, it begins to slow you down. You eventually reach a point where developers spend the majority of their time debugging, patching, and deciphering convoluted code, rather than innovating and building new features.

This can sometimes be a challenge because the effects of technical debt often compound slowly over time. Some issues, like minor inefficiencies, might seem insignificant at first but can gradually snowball into major bottlenecks.

Developer dissatisfaction, on the other hand, can be harder to detect early but often results in deeper organizational challenges. In companies with poor coding practices and excessive technical debt, it becomes increasingly difficult to attract top talent and even harder to retain them. Talented developers, frustrated by spending their time addressing technical debt instead of driving innovation and delivering value, may disengage. This disengagement often leads to turnover, which not only depletes the team’s expertise but also damages the company’s reputation, making it even harder to attract other top talent.

Coming Next: Technical Debt in the AI World

In the next part, we’ll explore Technical Debt in the AI world, with a specific focus on how Large Language Models (LLMs) can be a double-edged sword when it comes to managing technical debt.

• LLMs in coding: If not used correctly and responsibly, it can lead to a rapid accumulation of technical debt. However, when harnessed effectively, LLMs can also help reduce technical debt by automating mundane, repetitive tasks like documentation, code reviews, refactoring and testing.

• Fixing Technical Debt: We’ll explore strategies to plan where and how to fix technical debt. By being strategic about prioritizing fixes, teams can maximize developer time while ensuring their codebase remains healthy and maintainable.

Stay tuned to discover how LLMs can be integrated into a sustainable technical debt strategy, balancing their strengths and pitfalls to build systems that are as efficient as they are scalable.

Struggling with technical debt? Let’s fix it.

We help teams regain control of their codebase, ship faster, and stay ahead of technical challenges. If technical debt is slowing you down, let’s talk.

• LinkedIn: Jose Dominguez

• X: @LePeppManu

• Blar: blar.io

Thanks for reading!