Technical Debt as a Badge of Honor

Anyone who has worked in IT long enough knows that “technical debt” has a well-deserved negative reputation. It’s usually the result of the shortcuts companies take, the duct tape solutions, the code that makes engineers wince. It’s the mess that accumulates when teams move fast, and eventually it starts slowing everything down.

Here’s what I’ve come to realize, though: there’s another way to think about it. Complaining about technical debt is a privilege that only growing companies get to experience. It’s evidence that you made it far enough to outgrow your early solutions. Think of it like looking back on decisions you made years ago. If nothing makes you cringe, you probably weren’t taking enough risks, or you simply haven’t grown.

Technical debt is what happens when a company builds something quickly to solve an immediate problem, knowing full well it won’t scale elegantly in the future. Think of it like renovating a house while people are living in it. You do what works now, even if it means you’ll need to redo it properly later. It’s like building your dream home in your 20s. You can’t predict who you’ll be later in life, and you’ll likely need to course correct at some point in the future.

The Right Team for the Wrong Moment

Small startups and large enterprises have fundamentally different needs. Startups need to move fast, find product-market fit, and innovate before someone else does. Large companies need to scale reliably, operate repeatably, and minimize risk. That journey from startup to enterprise almost always generates technical debt along the way. The solutions that get a company off the ground rarely hold up at scale, That’s not a failure of foresight, it’s just the nature of building something real under pressure.

Consider this common scenario. Imagine a small startup that builds a 10-person development team to develop a web-based product. The team is fantastic: highly functioning, great synergy, appropriate structure, history of working together, and just about every attribute that looks perfect on paper.

Yet despite seeming to check every box, the team turned out to be the wrong fit. They came from large organizations where they built highly engineered, market-research-driven solutions with quarterly development cycles. What the startup needed was the ability to move fast and iterate weekly, not quarterly. This kind of mismatch is easy to miss in the moment, even for experienced leaders focused on doing the right things.

The real cost shows up in what doesn’t get built. When a team is focused on engineering solutions for problems that don’t exist yet, the challenges that actually need solving get pushed aside. By the time they’re addressed, the opportunity has often already passed.

Embracing the Mess

In my current role at DataBank, I’m part of a small, self-contained team working on managed services solutions. We’re allowed to be the experts and make calls quickly, but we have to deliver—and we consistently do, with speed and pragmatism over perfection.

We’re creating solutions that prioritize speed and iteration over elegance. They may not be the most refined builds, but they solve real problems now and create a foundation we can improve on as we grow.

I couldn’t be more excited about it.

Because those future problems? They’re the ones worth having—signs that we succeeded, that we grew, that we moved fast enough to capture opportunity before it disappeared. Technical debt means you’ve scaled far enough to outgrow the solutions that got you here.

The Potential for New Technical Debt

There’s an interesting parallel with what’s happening in AI-assisted development right now. Non-technical people (myself included) are using AI coding tools to create fast solutions. The code isn’t always elegant or scalable, usually leading to so much AI slop. It’s what happens when the priority is shipping fast over building well, and any team moving quickly enough is likely to encounter it.

The question worth asking is whether AI-assisted code represents the same kind of luxury problem we’ve been discussing. I think it does.

The deeper question is whether experienced programmers will evolve into specialists who help AI-generated code scale elegantly, or whether AI development tools will simply advance to the point where the quality gap disappears. The answer is probably both. There will always be value in building things that hold up over time, and there will always be value in empowering more people to move faster. In the early stages of a product or service, speed matters most. But the craft of building things that scale never really goes away.

The Bigger Picture

None of this is an argument for building carelessly. It’s an argument for building appropriately for the stage a company is in. A three-person startup shouldn’t be building like they’re Google. They also shouldn’t feel ashamed when, three years later, they need to refactor the systems that got them to product-market fit.

The technical debt conversation tends to focus on what went wrong. A more useful question is what went right. The team moved fast enough to gain traction. They built solutions that worked for the problems they had, not the problems they might face someday. They earned the right to revisit that early code.

The next time an engineering team starts groaning about technical debt, consider celebrating it instead. The company made it. There are real problems worth solving. Those problems can now be addressed with the time and resources that simply weren’t available in the early days.

That’s not a burden. That’s a badge of honor.

Jason Rasmuson also contributed to this article.