The Interest Is Compounding: All About Tech Debt

Transcript

Sid (00:09): Hey everyone. Welcome to Page to The Limit, where we dive into real stories behind digital operations, instant response and everything in between. I’m your host, Sid, and whether you’re on call or off duty, we’re here to bring you fresh perspectives from the team that keeps the world always on. Let’s get into it. Hey there. Today’s episode isn’t about a shiny new backend tool that’s trending this week. It’s not about the best way to increase shipping velocity or how to get another 5% out of your deploy pipeline. It’s about something more structural, something that doesn’t show up on dashboards but shapes almost everything your team does. Technical debt, we throw the term around a lot, usually with a shrug or a sigh, like it’s just part of some background noise, but under the surface, tech debt tells a story, a story about how your systems got the way they are, about what you prioritize and what you were forced to ignore.

(01:00): Today, we’ll unpack that story. What technical debt really is, how it happens, the different types. You should know about what it looks like when it turns dangerous and what it actually takes to pay it down. Not in theory, but in practice. Along the way, we’ll look at two case studies, one about a trading firm that collapsed under an hour because of a dormant feature flag. The other is about Dropbox and how they turned a massive infrastructure change into a rare chance to rebuild trust in their systems. But first, let’s get aligned on what tech debt means in the first place. The term technical debt was coined in the early nineties by Award Cunningham, and like all good metaphors, it’s been overused, the point of near meaninglessness, but ATS core, it’s simple tech debt is the cost of a system no longer matching the needs of the people building on top of it.

(01:50): It’s not necessarily about messy code or lack of comments, it’s about misalignment. Your architecture, your code base, your tooling. They’re all artifacts of past decisions, but if those decisions no longer reflect the current reality of your team, your product, or your business model, then you’re working against the grain and working against the grain is expensive. Debt lets you go faster in the short term. You delay a cleanup task, you cut a corner, or you take on some complexity, but don’t immediately resolve it, but eventually you’ll pay for it with interest, slower delivery, more bugs, higher friction, and an increasing sense that change is risky, not routine. Okay, so how does tech debt happen? Well, tech techit usually doesn’t happen all at once. It sneaks in through hundreds of small decisions, most of them entirely reasonable in isolation. A feature scope expands mid sprint and the clean abstraction you planned becomes a copy and paste job.

(02:45): A key engineer leaves the company and with them goes the only real understanding of how a critical iteration works. You move fast, the context shifts, documentation lags, and suddenly you have a subsystem that nobody really understands, but everyone on. Sometimes though the debt is intentional, you take a shortcut, ship, something messy and plan to revisit later, but more often it’s unintentional. It’s a byproduct of urgency or shifting ownership or of success. The kind where you move so fast, you don’t realize how much complexity you’ve inherited until you’re buried in it. Not all tech debt is the same. Some of it’s well considered, some of it is reckless. It helps to think about it in two dimensions, intent and impact. On one axis, you’ve got intentional versus accidental. Intentional debt is when you know you’re making a trade off. Maybe you skip writing tests for a small feature in order to hit a deadline.

(03:41): You then file a ticket. You flag it in code. It’s a conscious choice that you’ll revisit later. Accidental debt, on the other hand, tends to show up later when you discover that a seemingly good decision had hidden costs that are now locked into other systems. On the other axis, you have prudent versus reckless. Prudent debt is strategic. You took on the complexity with a clear goal in mind and ideally a plan to unwind it. Reckless debt happens when no one’s thinking about the consequences. There’s no plan, there’s no documentation, there’s no ownership. It just piles up untracked until it starts to affect everything. Now, most teams carry all four types, prudent, reckless, intentional, and accidental. The real scale is not pretending you’ll avoid the debt entirely, but recognizing that you’re carrying it and how urgently it needs to be addressed. Okay, now that we have an understanding of what tech debt is and how it comes to be, let’s talk about some case studies.

(04:49): First, let’s talk about Knight Capital. In 2012, Knight Capital Group was one of the largest trading firms in the United States. They handled billions in daily volume, and like many financial companies, they had a sprawling code base that had evolved over many years. That summer, they had developed a new software update designed to comply with regulatory changes. The rollout went into eight production service. On seven of them, everything was fine, but on the eighth, a dormant code path was accidentally reactivated. That code had been part of a discontinued feature, but it hadn’t been removed. It just been buried behind a feature flag that no one had properly documented or tested. When the update went live, the servers began flooding the market with unintended trades, buy high, sell low on repeat. The system lost $10 million a minute. It took them about 45 minutes to realize what was happening and pull the plug, but by then they had lost around $440 million.

(05:55): The company never recovered. They were forced to merge with a competitor and exit the market early, all because of one piece of legacy logic that had been quietly decaying in their code base, unnoticed and un unknown, ticking time bomb disguised as a forgotten flag. Of course, most debt doesn’t explode. It erodes slowly and invisibly and thoroughly. You start to notice that features take longer to build than they should have. Code reviews stretch out because no one really knows how a service is supposed to behave. New engineers take weeks to onboard because no one can explain how certain flows work, only that they work and they shouldn’t be touched. Teams begin to route around the mess. They avoid fixing broken abstractions. They stack new logic on top of old hacks, and eventually the workaround becomes the default. Technical debt isn’t just in the code at this point.

(06:49): It’s in the culture, so why is it hard to pay off? Well, everyone agrees that tech debt is bad, but when it comes to actually doing something about it, things get a little bit murky. Engineers file refactor tickets that sync to the bottom of the backlog. PMs ask for business justification and leadership asks what features you’re delaying in order to address the debt, and everyone agrees that we’ll revisit it after the sprint, which quietly becomes after the next quarter. The challenge is the pain of debt is rarely acute. In our night example, it was, but most of the time it’s diffuse. It’s not one bug. It’s dozens of minor and efficiencies that build up until your roadmap becomes a negotiation. With the past, paying it down doesn’t feel urgent until it really is. Now, let’s move on to our next case. Study about box and Magic Pocket.

(07:46): In 2016, box decided to migrate off of Amazon S3 and builder, their own storage infra, something that they called Magic Pocket. The move was partly about cost. S3 was expensive at their scale, but it was also about architecture. Over time, Dropbox systems had become deeply coupled to S three’s behavior. They were brittle wrappers around API quirks, duplicate error handling and inconsistent assumptions about eventual consistencies. The system worked, but it was delicate and poorly understood. The Magic Pocket migration was more than just data move. It was a clean slate. Dropbox used the opportunity to redefine internal contracts, standardize abstractions, and eliminate layers of duct tape that had quietly become mission critical. They didn’t just swap out one backend for another. They rebuilt trust in their own code base with their own engineers, and they did it without stopping the world. By dual writing to both systems, measuring drift in real time and instrumenting the transition like a product launch, it’s a model for what good pay down looks like.

(08:53): Not a refactor sprint that gets cut when deadlines are slip, but a strategic investment embedded into a larger goal. So what does paying down debt actually look like in practice? First, you need to make it visible. That means naming the problem. Clearly not this is bad, but this API takes three days to modify because its logic is spread across five unknown services. Then tie that pain back to impact. What features are getting delayed? What instance are more likely to occur and what’s the hidden cost of doing nothing? And the next assigned ownership debt without an owner is just entropy. And finally, connect the cleanup to forward motion. Is there a feature launch, a migration, every design coming up? Well, you can use that moment to your advantage and bury the cleanup work inside a strategic deliverable. It’s worth remembering though. You can’t prevent all debt, but you can slow it down and when it shows up, you can catch it early.

(09:53): There aren’t really any silver bullets. The best advice I can give you is make sure every service as a clear owner, make documentation a first class citizen, even if it’s incomplete, and create a culture where people feel safe flagging complexity, even if it means slowing down for a minute, and that’s it. It’s not fancy. It’s a series of small steps that you can take that will ensure the rate at which you accumulate debt slows down. Now, let’s start wrapping up. Tech debt isn’t a bug in the system. It is a symptom of growth. It tells you something about your team, about what you valued, what you postponed, and what you hoped would be someone else’s problem. And if you let it, it can quietly limit everything that you do. But if you name it, track it, and build a discipline to chip away at it, it becomes manageable, maybe even strategic, because clean systems don’t stay clean. They stay changeable, and that’s what really matters. Thank you very much for listening to this episode. If this made you think of a service that you haven’t touched in six months because you’re afraid of what lives behind, maybe this is your reminder to open that door.

(11:06): Thanks for tuning in to this episode of Pages of the Limit. If you enjoyed this episode, don’t forget to subscribe and share it with someone who would find it valuable. Thank you for listening. Until next time, stay resilient and keep those pages light.