I am strongly in favor of building software that can evolve.
Good systems should not collapse the moment a product changes direction. They should leave room for growth, make future changes possible, and avoid painting the team into a corner too early.
But there is a difference between building for the future and designing for an imaginary future.
That difference matters.
Overengineering is often discussed as a technical problem: too many abstractions, too much indirection, too many layers, too much architecture before the product needs it. But in product teams, overengineering is also a product risk.
It slows down learning. It makes teams less flexible. It increases the cost of change. It makes onboarding harder. It creates systems that are technically impressive but difficult for humans to understand, maintain, or safely modify.
And increasingly, it also creates systems that are harder for AI-assisted tools and agents to reason about.
Software is read more than it is written
Most software does not fail because the first version was not clever enough.
It fails because six months later, someone needs to change it and cannot easily understand how it works.
That person might be another engineer on the team. It might be a new joiner. It might be you, coming back to code you wrote months ago. It might also be an AI coding assistant trying to help with a refactor, a bug fix, or a new feature.
In all of those cases, clarity matters.
Readable code is not a nice extra. It is a form of product velocity.
When a system is easy to understand, teams can move faster with more confidence. They can debug issues more quickly. They can make smaller changes. They can review code more effectively. They can onboard people without relying on tribal knowledge.
When a system is over-abstracted, even simple changes become expensive. Engineers have to jump across files, layers, factories, adapters, configuration objects, and generic interfaces just to understand where the real behavior lives.
That cost compounds over time.
Abstractions should be earned
Abstraction is not the enemy. Bad timing is.
Some abstractions are extremely valuable. They reduce duplication, create consistency, protect business rules, and make complex systems easier to work with.
But abstractions work best when they are extracted from real patterns, not invented before the product has shown what it needs.
Early in a product or feature, the team is still learning. Requirements change. Users react differently than expected. Edge cases appear. Some ideas are removed. Others become more important.
At that stage, premature abstraction can be dangerous because it freezes assumptions too early.
The code starts to reflect a theory of the product rather than the product itself.
A more practical approach is to start with a clear, direct implementation. Let the shape of the product emerge. When patterns repeat, when the same concept appears in multiple places, when the cost of duplication becomes real, that is the right moment to introduce a stronger abstraction.
Not before.
Future-proofing should be plausible
One common reason for overengineering is the phrase: "We might need this later."
Sometimes that instinct is correct. Experienced engineers should think ahead. They should notice risks, identify likely scaling problems, and avoid short-term decisions that create obvious future pain.
But "we might need this later" needs pressure testing.
How likely is this future? What would it cost to add later? What does it cost to build now? Will this extra flexibility make today's product harder to understand? Are we solving a real upcoming problem, or protecting ourselves from uncertainty?
Good engineering is not ignoring the future. It is being honest about which future is likely enough to design for today.
A system can be simple and still be ready for change. In fact, simple systems are often easier to change because there are fewer hidden rules, fewer layers, and fewer assumptions built into the architecture.
Complexity has a user experience too
Engineers sometimes think of UX only as something users see on the screen. But code has a user experience too.
The users of code are other engineers.
They need to understand where to make a change. They need to know which abstractions matter. They need to see how data flows through the system. They need to reason about side effects. They need to know whether a small change is safe.
When code is unnecessarily complex, the developer experience gets worse. And when developer experience gets worse, product delivery gets worse too.
Bugs take longer to fix. Features take longer to ship. Reviews become slower. Refactors become scary. Teams become more dependent on the few people who understand the architecture.
That is not a sign of a mature system. It is a sign that the system has become expensive to think about.
Build for humans first
I believe software should be understandable by humans first.
That does not mean it should be simplistic. It does not mean avoiding architecture, patterns, or long-term thinking. It means the structure should help people understand the product, not hide it behind unnecessary cleverness.
A good system should make the common path obvious. The business rules should be easy to find. The naming should match the domain. The boundaries should reflect real responsibilities. The code should explain the product as much as it implements it.
This matters even more now that AI tools are becoming part of everyday development. AI agents are useful when the system gives them clear signals: consistent naming, straightforward flows, obvious boundaries, and tests that describe expected behavior.
If the codebase is full of accidental complexity, AI does not magically solve that. It can even amplify confusion by confidently editing the wrong layer or following a misleading abstraction.
Human-readable software is also AI-readable software.
Practical simplicity is not laziness
Sometimes simple solutions are mistaken for less serious engineering.
I see it differently.
Practical simplicity requires judgment. It means understanding the problem well enough to solve the real version of it, not the imaginary enterprise version. It means knowing when to add structure and when to wait. It means resisting the temptation to make the code look impressive at the cost of making it harder to change.
The goal is not to write the least code possible.
The goal is to write software that is clear, useful, maintainable, and ready for the next reasonable step.
That might mean starting with a straightforward implementation. It might mean duplicating a small amount of code until the pattern is real. It might mean using boring technology because the team understands it. It might mean avoiding a generic framework when a few explicit functions would do the job better.
There is nothing unambitious about that. It is often the fastest path to a product that can actually grow.
The balance I aim for
The balance I like is this:
Build for today's real problem. Leave room for tomorrow's likely problem. Avoid designing for a future nobody can describe clearly.
That balance is not always easy. It requires technical experience, product context, and honest conversations with the team. But when it works, the result is software that feels calm. It does what it needs to do. It is not fragile. It is not overly clever. It is understandable enough that others can continue improving it.
That is the kind of codebase I trust.
Not the one with the most layers. Not the one with the most patterns. The one where a tired engineer on a Monday morning can open the code, understand the intent, make a safe change, and move the product forward.