YAGNI Might Be Costing You More in the Long Run
The Hidden Costs of Ignoring Future Needs
In “agile” software development, one of the mantras that's been repeated so often it's practically dogma is "You Aren't Gonna Need It" — YAGNI. The principle tells us not to build features or abstractions until they're absolutely necessary for an immediate user story in the current sprint. In its original context, this was a reaction to speculative architecture and the kind of over-engineering that bloats systems, increases complexity, and slows down delivery.
But like many well-intentioned simplifications, YAGNI has been over-applied through the years. In the zeal to avoid complexity, many agile teams have begun to overlook the value of thoughtful foresight. There’s a wide gap between speculative, unnecessary features and strategic extensibility that helps systems successfully evolve and change. The key is not building everything upfront, but building with an awareness of where the system might need to go.
I've seen code reviews where almost any kind of extensibility — whether it's an extra interface, an API wrapper, or even a modular folder structure — gets dismissed by a YAGNI zealot. The result isn't leaner software; it's fragile software that resists change and creates technical debt that punishes developers down the line. Extensibility doesn’t have to mean overbuilding — it can mean making room for the system to grow.
In reality, many of the features and changes that a system eventually needs are not completely unpredictable early on in its lifetime. Often, the path of evolution is shaped by market realities, regulatory pressures, or business directions that are already known — just not accounted for or required in the initial version or MVP. When we ignore these factors under the guise of YAGNI, we risk building ourselves into a corner.
When that happens, one of the most common downstream effects is technical debt.
The Real Cost of Technical Debt
Technical debt doesn't just come from rushed or poor-quality code. It also accrues when systems are built without enough flexibility to adapt to well-understood kinds of change. This is the kind of debt that's most painful: the kind you could have avoided with just a bit more care.
Retrofitting extensibility into a mature system is expensive, risky, and often constrained by years of prior decisions. Code changes themselves — especially now, with the rise of high-quality AI coding assistants — are easier than ever. You can rename variables, extract functions, and generate boilerplate and entire functions with impressive speed. But code is only part of the story. The hard part is design and architectural extensibility: restructuring how components interact, changing boundaries, rethinking assumptions. That work still requires deliberate thought, coordination, and an understanding of long-term impact.
This distinction is critical. When YAGNI is applied too rigidly, it often leads to architectural decisions that are cheap in the moment but costly to change later. And unfortunately, the current generation of AI can't really dig you out of those kinds of holes.
A concrete example: At multiple points in my career, I observed teams that had built user interfaces with all text strings hard-coded in English. Naturally, there was no expectation of supporting multiple languages, so localization and internationalization were considered unnecessary overhead -- “YAGNI!”. But inevitably (as part of their success) the businesses decided to expand into new markets — and suddenly, supporting multiple languages became a top priority. Because the codebase hadn't accounted for localization from the start, teams had to scramble to extract strings, restructure UI components, and retrofit translation support. It turned what could have been a straightforward enhancement into a painful, expensive refactor. On top of that, the process introduced dozens of new bugs — everything from mistranslated strings to broken layouts and misaligned interfaces — all of which had to be tested, fixed, and re-tested under pressure. A lightweight internationalization framework early on would have saved substantial time, cost, and frustration.
These kinds of stories are common, and they illustrate the real consequences of short-term thinking. Whether it’s localization, authentication and authorization, cloud flexibility, or data modeling, the pattern is the same: Ignoring plausible future needs can turn routine changes into expensive rewrites. And while code itself might be easier to fix today, system design and architecture — the scaffolding everything hangs on — is as critical, and as difficult, as ever.
AI Coding Assistants Can't Help Much Here
In an era where AI-assisted coding tools are becoming the norm, it's tempting to think that many of these problems will just be automated away. But here’s the truth: AI can't reason about the future of your application or system domain.
AI is great at filling in boilerplate, doingn code reviews and suggesting refactors, and even writing whole functions. But asking an AI coding assistant to tell you what is and isn't a YAGNI feature is like asking it to predict the future — something it simply can't do. AI lacks the context needed to determine whether a feature or abstraction is truly necessary now or whether it should be deferred for future needs. It doesn't know that your industry might soon face new compliance requirements, nor does it understand that your business plans to expand into a new market segment next year. It can't weigh trade-offs in architecture based on the kind of soft knowledge or long-term trends that guide thoughtful decisions about what to build upfront.
Designing just the right amount of flexibility into a system — not too much, not too little — requires an experienced software engineer working in concert with domain experts. It’s a collaboration rooted in technical judgment and product intuition, not AI-enabled autocomplete.
Build with Foresight
The best systems aren't over-engineered, but they aren't naive either. They anticipate, adapt, and age gracefully. Getting there requires experience, discernment, and the ability to balance simplicity with preparedness.
If you're building a system that you expect to live longer than a sprint or two, let’s talk! I specialize in helping teams build thoughtful, maintainable architectures that support long-term agility without premature complexity. Let's work together to build something that lasts.
