Are We Building Software or Letting It Drift?

Imagine shipping features at a pace that felt impossible just a few years ago—prompt an AI, generate clean code, run the tests, deploy—everything flows smoothly, progress feels unstoppable. Until, months later, a seemingly minor change triggers a cascade of unexpected behavior that no one can fully explain.
Over the last few years, I’ve been watching a pattern repeat across teams and projects. It shows up in legacy-to-cloud migrations, but it doesn’t stop there. I see it just as often in brand-new systems built almost entirely with AI assistance — fast, confident, and delivered at a pace that would have been unthinkable a few years ago.
The development landscape has changed dramatically. The underlying risk hasn’t.
AI has made it easy to write a lot of code very quickly. Whether we’re migrating old systems or “vibe coding” new ones, the feedback loop feels great. You describe what you want, the AI produces something plausible, tests pass, and the system appears to work. Delivery feels smooth. Progress feels real.
And that’s exactly where things start to drift.
The issue isn’t that AI writes bad code. Often, it writes code that looks cleaner and more structured than what humans would have produced under pressure. The problem is that AI doesn’t just implement designs — it infers them. And every time we clarify, rephrase, or repeat a prompt, we give it another opportunity to reinterpret what the system should be.
Over time, the system quietly becomes something no one explicitly designed.
This is easiest to see during migrations. A system is supposed to be moved, not changed. But AI reorganizes modules for clarity, introduces abstractions that weren’t there before, and removes logic that appears redundant. Each change makes sense locally. Collectively, they reshape the architecture.
What’s more concerning is that the same thing happens in new projects — often faster. When a system is built incrementally through prompts and follow-up corrections, architecture emerges implicitly. There’s rarely a single moment where someone says, “This is the shape of the system.” Instead, the shape is the sum of many small interpretations made by an agent optimizing for the last instruction it received.
Repeated prompting makes this worse, not better. Each clarification nudges the model toward a slightly different understanding of intent. Boundaries blur. Responsibilities shift. What began as a clean mental model slowly fragments across files and layers.
One of the most visible symptoms of this drift is redundant and dead code. AI is remarkably good at adding logic to handle edge cases it’s no longer sure are covered. It duplicates behavior across components because it no longer sees the full picture within its context window. Over time, you end up with multiple versions of “the same thing,” none of which can be safely removed because no one is fully confident what depends on what anymore.
The code still works. That’s the trap.
Understanding it becomes harder with every iteration. Reading it no longer explains why the system behaves the way it does — only how it happens to behave right now.
Accidental refactoring is another quiet failure mode. You ask for a small change. The AI rewrites more than you expected because, from its perspective, consistency is improvement. Execution order shifts. Error handling changes shape. Responsibilities move just enough to matter later, but not enough to break tests today.
Nothing is obviously wrong, so it ships.
These problems don’t announce themselves immediately. Architectural drift rarely causes instant failures. What it does is erode the system’s ability to evolve safely. Months later, when performance degrades, or partial failures cascade, or a seemingly minor change triggers an incident, teams find themselves debugging behavior that no longer maps cleanly to any original design.
At that point, it doesn’t matter whether the system was migrated or greenfield. The problem is the same: the architecture exists only as an emergent property of code generated over time, not as an intentional, shared understanding.
Some teams try to address this with better prompts. Clearer instructions. Tighter constraints. That helps, but it’s not a solution. Large systems don’t fit into a single context window. AI fills gaps by inference. Redundancy creeps in. Dead code accumulates. Drift continues — just more quietly.
The uncomfortable truth is that AI doesn’t cause architectural drift on its own. Drift happens when intent isn’t made explicit and maintained over time. AI simply accelerates the consequences of that omission.
Used carelessly, AI makes it easy to move fast without realizing what’s being changed. Used thoughtfully, it can surface uncertainty and force conversations we’ve been postponing for years. But it cannot hold architectural intent on our behalf.
That responsibility doesn’t go away just because code is easier to produce.
If we treat AI-assisted development as a shortcut to delivery rather than an architectural event, we shouldn’t be surprised when systems become harder to reason about, harder to change, and more fragile over time.
The code will compile.
The tests will pass.
The problems will wait.
Architectural drift doesn’t break systems immediately. It just ensures that when they do break, no one knows what the system was meant to be or which assumptions still hold. Every change becomes risky not due to lack of skill, but because intent was never held steady while everything else accelerated.