Alex Antonuk:
– If you’ve worked in a growing startup, chances are you’ve witnessed this moment at least once.
Shipping features felt easy. Releases were frequent. Decisions were simple, almost like those glossy Silicon Valley legends. And then, suddenly, you find yourself in a reality where every release starts with “Just don’t touch anything.”
That’s when you realize you’re no longer developing the product but keeping it in an induced coma. And, of course, the one engineer who knows how to ship anything in this state is on holiday.
Scary? Same here.
But that’s what a product looks like when tech debt stops being a Reddit meme – and becomes an operational reality. It begins to directly affect how fast you move and how reliably the business runs.
At that point, hesitation is dangerous.
So can young teams avoid tech debt? And if not, what kind of debt is actually acceptable?
How do we make it visible? And how can we build repayment into our work, so we can keep developing the product instead of just patching holes?
Part 1 – What is technical debt and how to distinguish it from “bad code”
Alex Antonuk:
– Arguments about tech debt have been around for decades. Some authors call it the product’s silent killer, while others even argue it’s beneficial.
But this isn’t a debate for debate’s sake. The industry simply doesn’t have a single answer to this question: what actually counts as tech debt. And there’s no clear line between where tech debt ends and plain bad engineering begins.
That alone tells us there won’t be a silver bullet today.
We’ve pulled together research, common patterns, and practical approaches to show clearly how tech debt forms in startups, how to manage it, and what to do when it gets out of control.
But let’s go step by step. What do we actually mean by tech debt?
The term goes back to Ward Cunningham, who explained it with a loan metaphor – you take a shortcut to ship faster now, and later you pay “interest,” through extra fixes, higher risk, and code that gets harder to change.
Simple idea. But over time it’s grown into something much messier. Now people talk about architectural debt. Testing debt. Infrastructure debt. Documentation debt. Some group these together, and others keep them separate. There’s also non-technical debt: broken processes, toxic culture, knowledge hoarding, and more.
One thing holds across all of it: tech debt is not a monolith. It shows up in layers. You pay for each layer differently – in time, risk, and, sometimes, in product momentum. Until a rewrite becomes the only way out.
Accenture landed on one of the broadest definitions out there: tech debt is the accumulated cost – in money and effort – to keep your IT systems current and able to meet requirements.
Yet, if you’re actually writing code, definitions like that don’t help much. People still argue. Does tech debt cover only intentional shortcuts? Or sloppy work and plain negligence, too?
We decided to ask Bamboo Agile’s dev team about their understanding of tech debt. First, we spoke to Vasilij Ninko, our Engineering Manager.
Vasilij Ninko:
– I’d suggest conducting a “Does it work?” test. If a solution with poor-quality code works now but may not work in the future, it’s more likely to be technical debt. In other words, it may be a high-quality solution that will still be hard to support and expand later on.
If the solution is already unstable, even though it quickly meets current needs, it’s bad code and poor development.
Alex Antonuk:
– Vasilij’s senior colleague, Bamboo Agile’s CTO Maxim Leykin, agrees but offers a sharper angle.
Maxim Leykin:
– I’d suggest thinking of technical debt as anything we knowingly put in as a trade-off – moving fast now but planning to fix it later with a better solution. This approach shouldn’t break anything today or cause immediate problems.
Alex Antonuk:
– Finally, Bamboo Agile’s Engineering Manager Alexey Shinkarev doesn’t focus on defining technical debt exactly. Instead, he cares more about when and under what conditions something actually becomes technical debt.
Alexey Shinkarev:
– I don’t really separate technical debt from bad code or unfinished work. For me, technical debt covers just about anything that goes wrong, like bad requirements, poor planning, sloppy design, weak architecture, or deadlines that nobody could meet. But here’s the thing – it only turns into real debt when you try to maintain or build on that code later. That’s when you realize you’re stuck until you go back and fix things.
Alex Antonuk:
– So, to put it simply.
Technical debt is actually a mix of choices and compromises – sometimes intentional, sometimes not – that end up making future work more expensive. These costs pile up across different technical layers and often spill over into non-technical areas too.
In the next part, we’ll look at how technical debt shows up in startups and why it gets aggressive fast.
Part 2 – How technical debt accumulates in startups
Alex Antonuk:
– Okay, so we’ve covered the basics of technical debt. But how does it appear in startups, and what type of it is the most dangerous?
Technical debt in big companies is usually just old complexity. Systems get overloaded, and the deeper problems become so hard to spot that fixing them feels impossible. Also, convincing the business to care is a tough sell.
In a startup, the story is different. Debt comes from speed. Growth brings constant pressure and change, which makes the debt aggressive.
Studies from Swedish universities show a typical pattern. Early on, the pain is low. So teams take on debt deliberately, trading quality for speed. But the turning point is scaling – that’s when risks start piling up fast. The bigger the team, the harder it is to keep debt under control.
Why? There are several reasons for this:
- New people join faster than you can properly onboard them
- Early clients have vague requirements or bring more and more edge cases
- Automation gets introduced with bugs
- Investor deadlines start creeping up
- Security gets skipped or rushed.
At this point, the debt depends entirely on who is on the team. And here, experts agree – taking on debt deliberately is often the only way to survive. Rules are unclear, and you need to stay ready to adapt. So teams cut corners, with simpler code, fewer tests, and faster fixes.
Because if you miss a release window early on, the whole thing can fall apart. And overengineering? That can kill a startup just as fast as debt.
Vasilij Ninko comments.
Vasilij Ninko:
– If you realize that the deadline is critical, it may be important to allow yourself to increase technical debt slightly. However, there’s one key condition – after the release, the most important parts of the debt should be repaid.
Alex Antonuk:
– Ignoring this rule buries your technical debt, and experts call this a major startup mistake. People often point to common mistakes teams make, like skipping documentation or hardcoding things that should be configurable.
According to Maxim Leykin, startups make these mistakes because everyone is too excited about the idea to do boring but necessary work.
But it’s not always the team’s fault. Back in the 70s, Manny Lehman pointed out that software naturally gets messy over time unless you actively stop to clean it up.
Why? Two things.
First, you can’t really measure productivity, so you never know for sure what will create debt later.
Second, in a fast-growing startup, the business changes faster than the code. What worked fine yesterday might break things tomorrow. That’s when free credit suddenly turns expensive.
So even if you think your tech debt is manageable, it’s better to avoid it in the first place.
But what kind of technical debt is most common in startups?
Maxim believes that technical debt is related to testing and documentation. At the same time, the most dangerous type is architectural debt.
Maxim Leykin:
– Test debt? That’s when you skip writing tests, rely too much on manual checks, or leave big gaps in coverage. Documentation debt happens when the only place critical knowledge lives is in someone’s head, and the docs are either outdated or don’t exist at all.
Both slow down development, make it harder to bring new people on board, and hurt product quality.
But the worst kind of technical debt, for me, is when you pick the wrong architecture early on, like building a monolith when you should’ve gone with microservices, just to ship fast. Once you go down that road, turning back is almost impossible. Refactoring becomes too risky and expensive.
Alex Antonuk:
– Vasilij Ninko points out that startups often take on technical debt simply when they choose a database or design a pattern that works for today but might hurt later.
Another common source is architectural debt, which usually comes from relying on paid third-party services. The pricing works fine at first, but as the user base grows, it can quickly spiral out of control.
So, if you’re building a startup, you’re almost certainly going to accumulate technical debt. That’s just the nature of the game. The real challenge for a team is to learn how to live with it, keep it visible, and treat it as part of the bigger picture, right alongside cultural and communication debt.
Most technical debt isn’t a single catastrophic failure, unless you picked the wrong architecture from day one. It’s more like millions of small, day-to-day compromises. Each one makes sense at the moment. On top of that, you have all the hidden complexity that no one notices until it starts causing trouble.
Over time, these small cuts add up. The product loses its predictability, and that’s the real price you end up paying.
Part 3 – Practical management of technical debt
Alex Antonuk:
– As we’ve learned, technical debt in a startup isn’t some kind of anomaly; it’s just part of the deal. You can’t avoid it, but you can manage it.
The question is: how?
We looked at a bunch of research and real-world practices, and boiled it down to three main pillars: visibility, prioritization, and a steady rhythm of maintenance.
Let’s start with how you manage technical debt every day, before it blows up.
From what we’ve read and heard from engineers in the field, there are six things you should put in place.
First, stop talking about technical debt in vague terms. You need a real way to track it. A registry, a shared language, or maybe a set of criteria that helps you actually assess how bad something is and what it costs you.
The Software Engineering Institute highlights a simple but powerful point – treat technical debt as a list of properties. For each one, write down what it is, the affected artifacts, triggers, ownership, and its consequences.
Let’s say you have a billing service with no contracts and no tests for critical stuff. You and your team agree that this causes two regressions a month. Each release needs a day or two of manual testing. There’s a clear owner. And you define a trigger – maybe two incidents a month, or a 20% bump in lead time. Now it’s real, not just “we have debt.”
Second, connect technical debt to the cost of making changes. Instead of tracking how fast you write code, track how expensive it is to change a line of code. Accenture says this is the single best way to tell if your system is getting healthier or falling apart.
Third, make debt repayment a habit. Set a budget and a schedule. Accenture suggests aiming for 15 to 20% of your total budget going toward paying down technical debt. That’s what most companies surveyed ended up doing. It’s not a hard rule, but it’s a good starting point.
Fourth, decide what stays protected. Even when things get messy, some things should never break. Stable CI. Basic regression tests for critical flows. Observability. Safe rollbacks. These are your guardrails.
Fifth, be realistic about AI and don’t let it run wild. Deloitte warns that AI is becoming a major source of new technical debt. Especially when people use it to make architectural calls, fall into vibe coding, or just let it run without oversight. That said, if you use it in focused ways, like refactoring or writing documentation, it can actually help clean things up.
Sixth, don’t ignore the human side. Organizational friction and communication gaps create technical debt faster than you think. If your team is overloaded, the interest keeps piling up while you’re busy somewhere else.
So that’s the “normal mode” playbook. But what about prioritization? How do you decide what to tackle first?
Here’s what Maxim Leykin has to say.
Maxim Leykin:
– While the specifics of prioritization depend on the project, you should usually tackle security and performance debt first. Another high-priority category is foundational technical debt: areas that require refactoring and on which future features depend. Paying that down early reduces risk and keeps development moving.
Alex Antonuk:
– Well, we hope these tips will be useful to startups. As for me, there is something to think about.
But what if there’s no time to think because the product’s technical debt is already in the red zone?
Let’s move to the fourth part of the podcast and find an emergency exit together.
Part 4 – Technical debt in a critical phase: emergency response
Alex Antonuk:
– Probably the most important step in dealing with accumulated technical debt is realizing you’ve hit a breaking point. And as we said, that’s not always easy to spot.
Experts and researchers suggest watching for five red flags. Some show up in how the team behaves, others are more technical.
First, people get nervous about releasing. A deployment stops being routine and turns into this big event everyone talks about and keeps putting off.
Let’s hear Alexey Shinkarev’s opinion.
Alexey Shinkarev:
– Listen to your team. If you hear things like “I’m scared to touch A, as I have no idea how it’ll affect B,” “Ask A, he’s been working on it,” “We can’t ship without A,” “Let’s push the release back a week,” then you’re in the red zone and need to act fast.
Alex Antonuk:
– Not sure if this is a technical or a communication problem? Try tracking your recovery time and reviewing it after each release. If your MTTR goes up while your deployment frequency goes down, you’re probably in trouble.
Second, lead time grows when predictability drops. Sure, not every increase in lead time means tech debt. But if a task that used to take “about two hours” suddenly starts taking two weeks, something is off.
Keep an eye on how much of the total task time is actual coding. If friction goes over 70%, that’s a red flag.
Third, you’re losing control over KTLO. Track how much time your team spends on incidents and manual work versus new features and real improvements. If the balance starts tilting toward the former, that’s a sign you’re slipping into the red zone.
Fourth, watch out for the bus factor. Don’t let critical knowledge sit with just one person. Build a simple competency matrix for each module or service. There should be at least two people who know how it works.
Fifth, every change comes with a tax. The debt has reached its terminal stage, when it touches architecture, tests, CI/CD, docs, and processes. One change triggers a dozen others.
So, the signals are clear. You’re in the red zone. What now?
The biggest trap here is treating this like “we need more refactoring.” Another trap is panicking and deciding to rewrite everything. Even if you pick the right parts to rewrite, you’ll probably fail.
Your mission now is to make the system manageable again, and you have to approach it from all sides.
This kind of shift usually comes down to three things. Stabilize first. Then go after the biggest pain points. And then lock in a working mode so you never slip back into firefighting.
Alexey Shinkarev suggests starting with a short audit. Figure out how critical each problem actually is, list all the debt items, estimate them, and connect each one to business impact. Not for the sake of paperwork, but because in the red zone you can’t afford to make decisions based on gut feel.
Then, as Vasilij shares from experience, put together a list of specific changes that actually fix the debt and ship them in isolated releases.
One release, one debt item. No new functionality. You can build new features in parallel, but don’t mix them into the same release. Otherwise, you blur the results, make rollback harder, and lose control again.
Bamboo Agile’s CTO adds details.
Maxim Leykin:
– I use a strategy we call “3+1.” Our team gathers all technical debt tickets on the JIRA board and plans our sprints in a repeating cycle. Three sprints focus on product features, then one sprint is dedicated to technical debt. In that fourth sprint, system architects and tech leads decide what to tackle first based on importance and complexity. The business doesn’t get a say there.
Alex Antonuk:
– Well, if we have to sketch out a quick plan for 2–4 weeks based on everything I mentioned, it might look something like this.
Week one is all about stabilization. You fix ownership, pick one anchor metric for predictability, like release stability or regression frequency, and make a shortlist of the most expensive of those metrics.
Then, you freeze anything that piles on debt faster than you can handle it. That means no major features that won’t pay off for weeks, no big rewrites without a clear win, and no new hacks or exceptions.
Week two, you go after the quick wins where the percentages hurt the most. Fix the delivery pipeline, lock in critical tests on the main flows, and clean up one or two toxic nodes or dependencies. The goal here is to get stable enough to move again.
Weeks three and four, you move from emergency mode to stability mode. As your first step, put minimum rails in your Definition of Done. Then, set a policy against new hacks. Proceed with carving out a fixed slice of capacity for debt. And finally, update the roadmap based on what you can measure.
And yeah, you’ll need to explain all this to the non-technical side of the house. As we said before, when business leaders don’t buy in, you end up burning time you could’ve spent actually fixing the debt.
Maxim Leykin comments on this.
Maxim Leykin:
– The best way to have this conversation is to show what ignoring technical debt actually costs – lost revenue or a damaged reputation. Take an outdated authorization library with weak security. That’s not an abstract problem but a data leak waiting to happen.
Alex Antonuk:
– Accenture also suggests splitting the debt discussion into four separate buckets:
- Direct support costs, or the debt itself
- Interest from lost productivity
- Blocks to commitments and security risks
- Missed opportunities.
The idea is to frame tech debt as an investment with a return, not a cleanup with a price tag.
Along with your roadmap, give management a registry that focuses on ROI.
So, to wrap up this big topic in one sentence, technical debt is nothing to be ashamed of. It’s part of your strategy, as long as you keep it under control. In a startup, you almost always trade quality for speed. That’s fine, but it should be a conscious choice, not just “living for today.”
Yet, you won’t always catch every bit of technical debt. So do this:
- Keep your engineering discipline
- Make tech debt visible
- Prioritize based on business impact
- Keep an eye on danger zones.
Maxim Leykin shares his own recommendations:
- Have senior devs and tech managers talk openly about technical debt with the whole team, including business and product people
- Log every tech debt issue in your task tracker, even if you don’t plan to touch it soon
- Let architects and senior engineers take the lead on prioritization
- Set aside time to gradually repay tech debt. Better to chip away regularly than wait for a “big refactoring” that never comes
- Don’t confuse tech debt with improvements or optimization.
I think these are really useful points.
