I still remember the days when I worked at a large insurance company, the kind of enterprise where COBOL still runs in the core systems, quietly but powerfully. No one dared to touch that code. It was the kind of legacy that everyone respected, feared, and tiptoed around.
At that time, if you asked any engineer why we never refactored it, the answer was simple:
“No one really knows what will happen if we do.”
The system worked.... barely, but it was a black box of decades-old logic, dependencies, and undocumented behaviors.
The Satellite System Problem
Since no one dared to modify the core, over the years, people started building satellite systems around it, smaller applications that added new features faster, without waiting for the core to evolve. It was pragmatic. Business got what it needed, engineers could deliver something visible, and everyone avoided the monster in the basement.
But after a few years, even the satellites started to show cracks. These systems became complex in their own right.... Full of duplicated logic, inconsistent data flows, and patches built on patches.
At one point, I proposed refactoring one of these satellite systems. Everyone hesitated. “Too risky,” some said. “Too much work,” said others. But the system was becoming unmaintainable, and new features took forever to build.
We decided to do it anyway.
It took six months. Six painful months of long hours, regression testing, and endless rounds of debugging. Along the way, we discovered an absurd amount of duplicate logic, dead code, unused modules, and plain rubbish.
In the end, we pulled it off. The system became faster, cleaner, and easier to understand. But it came at a massive human cost — time, effort, and anxiety.
Now, Doing That with AI
Fast forward to today.
With AI-assisted development, refactoring that same system would look completely different.
AI tools can now analyze the structure of codebases, map dependencies, and summarize business logic with impressive accuracy. They can generate or improve test cases automatically, and even suggest refactoring strategies that would take a human team weeks to plan.
When you have an intelligent system that can:
- Understand code semantics, not just syntax
- Identify duplicated logic across thousands of files
- Auto-generate integration tests
- Suggest consistent naming conventions and abstractions
…then the cost of refactoring drops dramatically.
Suddenly, legacy systems are no longer sacred monuments — they’re just old code that can be replaced, rewritten, or modernized in a fraction of the time.
And that changes everything.
Software with an Expiration Date
Historically, software longevity was a badge of honor. We built systems to last. Stability was everything.
But what if that assumption no longer makes sense?
If AI can rewrite or refactor systems easily, the lifespan of software will naturally get shorter. The value shifts from building something “permanent” to building something “adaptable.”
Software might become more disposable??!! Actually.. that’s not necessarily a bad thing.
When refactoring and rewriting are cheap, there’s less reason to over-engineer architectures for long-term maintainability. Instead of spending months perfecting the perfect layered model or microservice pattern, we might favor simpler, more generic designs that are easy for AI to understand and modify.
Rethinking Software Architecture
For decades, our industry has cycled through design philosophies — from monoliths to microservices to service meshes to event-driven architectures. Every few years, we rename or repackage the same ideas: separation of concerns, modularity, reusability.
But if AI becomes a key player in maintaining and evolving systems, maybe the best architecture is the one AI can reason about most easily.
That means:
- Less clever abstractions, more transparency
- Simpler patterns, fewer custom frameworks
- More consistent conventions, fewer human quirks
We might design software not just for humans to maintain... but for AI to understand, refactor, and evolve continuously.
In that world, architecture becomes less about rigidity and more about fluidity. You don’t freeze designs for a decade, you evolve them weekly, safely, and automatically.
The Human Shift
This doesn’t make engineers obsolete, it just changes what we focus on.
Instead of fighting technical debt manually, we’ll define refactoring policies and semantic constraints that guide AI. We’ll spend more time thinking about business logic, domain modeling, and user needs, while AI handles the plumbing, code cleanup, and modernization.
The skill set shifts from “how do I write this class elegantly?” to “how do I describe this system so that AI can evolve it intelligently?”
Where Human Expertise Still Reigns
Of course, this vision of disposable software doesn’t apply everywhere. Not all code will become a generic, AI-managed commodity. Deep human intervention will remain non-negotiable in specialized domains where performance and precision are paramount.
Think of high-frequency trading platforms, core engines, or kernels. In these arenas, engineers aren't just writing logic, they're squeezing every last nanosecond of performance from the hardware. This requires a level of bespoke optimization and creative, low-level mastery that AI can assist with but not lead. The same holds true where knowledge is highly enclosed within a specific domain, like the firmware for medical devices or avionic systems. In these mission-critical cases, the final architecture and code will always be shaped by human expertise, accountability, and deep domain knowledge.
The New Philosophy of Software
In the AI era, the best codebase might not be the most elegant or the most performant, it might be the one that’s most refactorable.
We’ll stop thinking of software as monuments and start treating them like living organisms, constantly regenerating, adapting, and shedding obsolete parts.
The irony? AI might finally allow us to break free from the legacy trap... from those untouchable COBOL systems that still haunt large organizations.
The systems of the future won’t last 30 years. They might last three and that’s perfectly fine. Because when refactoring is cheap, software doesn’t need to live forever.
Back then, refactoring that insurance satellite system was a heroic project. Today, it could be a weekend job... Not because we’re smarter, but because our tools are.
So maybe the right mindset going forward isn’t “build to last,” but “build to evolve.” Software’s new strength won’t be in its permanence, it’ll be in its disposability.
Disclaimer: The opinions expressed in this post are my own.
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