What is technical debt? And why does almost every startup have it?

In the fast-paced world of startups, the race to get a minimum viable product (MVP) to market and prove a business model often leads to cutting corners in software development. These shortcuts tend to accumulate in the form of "technical debt" – a term coined by famed programmer Ward Cunningham to describe the long-term costs incurred by expedient yet suboptimal technical decisions.

Almost every startup accumulates technical debt in the early stages, using it as a tool to move quickly despite limited resources. However, this debt must be managed carefully to prevent it from crippling productivity as the codebase grows. Understanding technical debt is crucial for engineering leaders trying to strike the right balance between speed and sustainability.

The financial debt metaphor

The concept of technical debt borrows from the world of financial debt. Just as taking out a mortgage allows buying a house now and paying for it over many years, accepting suboptimal code allows releasing software faster at the expense of having to fix issues later.

All debt has a cost in the form of "interest" – ongoing negative consequences until the principal is paid off. For financial debt, the interest is monetary. For technical debt, it manifests as slower development velocity, increased bugs, and frustrated developers. Compounding interest means that ignored debt tends to spiral out of control.

However, technical debt differs from financial debt in several ways. It‘s often unintentional and less visible to stakeholders. There‘s no legally required payment plan or due date. The impact and interest are more variable over time.

How technical debt arises

There are many ways that technical debt can creep into a codebase, with startups being especially susceptible due to tight deadlines, pivoting requirements, and lack of formal processes. Some of the most common sources include:

• Intentional shortcuts: Hacks and band-aid fixes used to hit a release date
• Poor coding practices: Sloppy, uncommented, and poorly documented code
• Inadequate testing: Lack of unit tests, integration tests, and test automation
• Design issues: Tightly coupled modules, improper abstractions, and rigid architecture
• Legacy dependencies: Outdated third-party libraries and frameworks
• Knowledge silos: Information not shared between team members

Technical debt often results from an intense focus on speed. In the words of LinkedIn founder Reid Hoffman, "If you‘re not embarrassed by the first version of your product, you‘ve launched too late." Getting an imperfect version to users is prioritized over code quality.

Technical debt also tends to compound on itself, making it increasingly difficult to implement new features. As Trey Huffine, a seasoned software engineer and startup veteran, explains: "Technical debt multiplies other technical debt. Building on technical debt and/or adding more debt typically causes an exponential growth in bad code."

Quantifying technical debt

While technical debt may seem abstract, it can actually be quantified and visualized. One common approach is classifying debt into a quadrant model based on whether it is deliberate vs inadvertent and prudent vs reckless:

Source: Martin Fowler

Deliberate and prudent technical debt is essentially taking out a loan to achieve a short-term goal with a concrete plan to pay it back. On the other end of the spectrum, inadvertent and reckless debt is "the kind of debt that really does bite you in the backside."

Code quality metrics offer another way to measure technical debt. Tools like SonarQube, CAST, and Kiuwan analyze source code to score maintainability, reliability, and security. Comparing metrics like cyclomatic complexity, code duplication, and dependency ratios against industry benchmarks give an objective sense of technical debt.

Source: SonarQube

Tracking metrics over time can show whether technical debt is increasing or decreasing. A 2020 study by Stepsize found that over 50% of development time is wasted due to technical debt, at an estimated cost of $85 billion annually. Surveys by OutSystems and Stripe report that 69% of organizations say technical debt is a major threat to their success.

The necessity of technical debt for startups

In the early stages of a startup, speed is often the top priority. The Lean Startup methodology popularized by Eric Reis emphasizes the importance of rapid experimentation and iteration to find product-market fit. This necessitates taking on technical debt to test hypotheses before running out of runway.

"Customers don‘t care what your code looks like. They just want your product," notes Huffine. "Startups should move quickly and test their ideas in the marketplace. Once they validate an idea, they should seek to understand the problem and the proper abstractions to scale it."

The key is ensuring that the debt taken on yields a higher return than the total cost. For instance, an e-commerce startup might hard-code payment processing for the first hundred transactions to start generating revenue. Once demand is proven, they can invest in integrating a robust payments platform.

Many hugely successful companies relied heavily on technical debt to accelerate early growth. Facebook famously followed a "move fast and break things" philosophy as it raced to dominate the social media market. Twitter‘s 2013 IPO filing disclosed "a number of areas that could potentially be exploited by attackers" due to "a development environment that valued ease of deployment over security."

The long-term risks of technical debt

While some technical debt can be a useful tool for startups, it becomes increasingly dangerous the longer it accrues. Unchecked technical debt acts as a constant drag on development velocity. Seemingly simple features require massive rewrites. No one fully understands the intertangled system.

The psychological effects on developers can be severe. Working with legacy spaghetti code is incredibly frustrating and demoralizing. Gartner reports that poor legacy code caused 30% of developers to leave their job within a year. Technical debt makes it difficult to attract and retain top engineering talent.

Technical debt also poses an existential business risk by inhibiting the ability to adapt to market changes and fend off competitors. A famous example is Nokia being blindsided by the launch of the iPhone in 2007. Despite having far more resources than Apple at the time, Nokia was crippled by years of mounting technical debt in its operating system, preventing it from pivoting to touchscreen smartphones in time.

Highly complex monolithic architectures are especially susceptible to technical debt. Microservices offer improved modularity but come with a new set of technical debt challenges around cross-service communication and orchestration. Migration between architectures is often a multi-year undertaking.

As Huffine warns, "At that point it can become the biggest impediment to progress. Until it‘s not. At that point it can become the greatest expense or even the cause of the death of the organization."

Managing technical debt

The longer technical debt lingers, the higher the risk. As a startup scales, it becomes increasingly critical to quantify and communicate the costs of suboptimal code to the entire organization. Technical debt must be made visible to ensure it‘s prioritized against new feature development.

Many engineering teams track technical debt as "chores" or "improvements" in their project management software. The goal is to ensure some portion of each sprint is allocated to refactoring. Maintaining a dedicated backlog for technical debt enforces constant attention.

Source: Atlassian

Some teams even adopt an "SOS" model: Ship, Observe, and Shift. Features are shipped to production quickly, observed for bugs and performance issues, and then shifted to a higher quality implementation. The process of paying down technical debt is institutionalized.

Adopting software craftsmanship best practices also goes a long way in preventing technical debt from getting out of hand:

• Maintaining coding standards and style guides
• Conducting regular code reviews
• Writing unit tests and integration tests
• Updating dependencies proactively
• Refactoring opportunistically
• Investing in automation and tooling
• Sharing knowledge through documentation and mentorship

Cultural changes that promote long-term thinking over short-term hacks are essential. Facebook ended up shifting from "move fast and break things" to "move fast with solid infrastructure." Learning when to pay down technical debt is a key skill for maturing engineering organizations.

Case studies

While almost every startup accumulates technical debt, some are able to successfully manage it as they scale. These companies balance speed with sustainability, using technical debt sparingly to achieve business goals while investing to keep it under control for the long run.

One example is Airbnb. In their early days, they had significant technical debt because "they were building things as fast as possible and learning along the way." As they reached hypergrowth, they began allocating 20% of each sprint to addressing technical debt. They also built an internal service ownership platform to help manage dependencies between their microservices architecture.

Uber is another instance of using technical debt to fuel rapid expansion. They consciously chose a monolithic architecture because it optimized for developer productivity when they were focused on entering new markets. However, they simultaneously invested in a massive suite of developer tools and infrastructure to modularize the monolith as they grew. Today, Uber has over 1,300 microservices supported by a platform that handles trillions of messages.

Conclusion

Technical debt is a constant concern for software startups trying to balance speed and stability. It‘s a major factor in 90% of software projects going over budget and 94% of large software projects failing.

But with the right processes and perspective, technical debt doesn‘t have to be a death sentence. It can actually be a powerful tool for rapidly validating ideas and establishing a market foothold. The key is ensuring that it‘s used tactically, tracked rigorously, and paid off continually.

"Understand it, control it, and use it as a tool, and it will help you build your startup," advises Huffine. "Every startup is in a technical debt rat race against their competitors and their market. The winners aren‘t the ones with the least technical debt, but the ones who manage it the most effectively."