Technical Debt Does’t Stay in the Code
We usually talk about technical debt as something purely technical, something only the engineering department cares about deeply. But the reality is it doesn’t stay there.
It spreads into planning cycles. It changes how teams estimate. It shapes who gets trusted. It alters escalation patterns. Eventually it becomes organizational structure.
Once upon a time, an engineering team owned a shared library that required updates across every consuming service whenever changes were made. Each change to the library required PRs with owning teams, triggered feedback loops across multiple services, and introduced coordination overhead measured in weeks.
Over time the engineers on the team avoided recommending solutions that depended on the library at all.
Eventually the library was converted into a service. The team had to make the case for time on our roadmap, showcasing the data of just how much faster we would ship every subsequent feature if we took the time to make this change.
The scariest part of this story is that before reducing the technical debt the engineers weren’t choosing the best solution. They were choosing the least expensive coordination path. And I couldn’t blame them. As their manager I also wanted to avoid that library.
I hadn’t realized how much technical debt was impacting the team. It wasn’t just an engineering “nice to have.” Technical debt had narrowed the solution space. At some point it had stopped being just technical debt and had become organizational debt — not because it was legacy code that annoyed engineers, but because it changed behavior across teams. Because technical debt had begun silently steering architectural decision-making.
On another team, only a subset of engineers deeply understood the system they were responsible for. This created implicit dependencies: reviews routed through those specific engineers, estimation confidence depended on them, roadmap sequencing depended on their availability, change safety depended on their presence, onboarding slowed, cross-team collaboration narrowed.
When faced with this scenario, resolving it became my top priority. Our velocity was a snail’s pace, but equally important there was no space for a marketplace of ideas. No one knew enough to contradict those with deeper knowledge. As a manager all I could think about was a clock ticking until one of these critical engineers left the team and our ability to ship ground to a halt.
This wasn’t just a knowledge gap. It was an organizational dependency structure created by technical complexity. Technical debt wasn’t just shaping delivery time. It was shaping authority and risk ownership. In critical legacy systems the solution is usually a combination — chip away at technical debt while simultaneously investing in learning for newer engineers, until I no longer heard that ticking clock.
None of these stories looked like technical debt problems at first. They looked like roadmap tradeoffs. They looked like staffing risks. They looked like cautious estimates. It took me time to realize they were all the same story. The library story is about debt shaping what gets built, the knowledge story is about debt shaping who gets trusted.
By the time technical debt reaches that point, it isn’t just slowing teams down. It’s shaping how the organization works.
Technical debt doesn’t just live in the code. It becomes the org chart.
