A few weeks ago, someone I know a smart, capable engineering lead told me about their team’s strange success story.
They deleted 200,000 lines of AI-generated code.
And their app still worked.
That alone tells you everything you need to know about the quiet cost of unchecked AI-assisted development.
The project had originally been around 100,000 lines already a decent size for what it did. But over time, it ballooned to more than double that number. Most of the bloat came not from features or performance improvements, but from auto-generated boilerplate, duplicated logic, and abstractions no one really understood anymore.
When they finally audited the system, they realized how much noise had crept in how much invisible entropy had been introduced under the banner of “productivity.”
They cleaned it up. They deleted code. They refactored by hand. And the product kept running, smoother than before.
The Illusion of Productivity
This is the side of AI coding no one talks about.
Yes AI can make you faster. But “faster” at what, exactly?
If your processes, architecture, and reviews are already weak, AI will accelerate your chaos. It doesn’t understand your domain. It doesn’t see the trade-offs. It just predicts what “looks right.”
And that’s exactly the problem: AI-generated code looks right.
It compiles. It passes shallow tests. It feels complete.
But under the surface, it’s often redundant, brittle, and opaque a kind of technical debt that doesn’t announce itself until you try to build on top of it.
I’ve seen teams overwhelmed by maintenance of code they didn’t truly write.
I’ve seen projects bloated with functions that appear useful but contribute nothing.
I’ve even seen leaders puzzled when productivity metrics looked great while actual delivery velocity slowed to a crawl.
The AI didn’t break the system.
It just quietly magnified the team’s existing weaknesses.
AI Is a Force Multiplier not a Substitute for Discipline
This story reinforced something I’ve believed for a while:
AI won’t fix your architecture.
It won’t make your team more thoughtful.
It won’t improve communication.
And it definitely won’t tell you when the thing it just generated is complete nonsense.
If your engineering culture is strong clean codebase, thoughtful design reviews, experienced developers who understand trade-offs then AI can be a genuine accelerant. It can help prototype ideas, fill in routine boilerplate, or refactor safely with guidance.
But without that foundation, AI becomes an amplifier of dysfunction.
It scales everything the good, the bad, and the ugly.
The Temptation of the “Autonomous Engineer”
I understand the temptation.
The promise of AI development tools is seductive: faster output, lower costs, instant scaffolding.
But I’ve learned that software isn’t about writing more code it’s about writing less code that does more work.
The best engineers I’ve worked with are ruthless editors.
They remove complexity.
They delete unnecessary abstractions.
They value clarity over cleverness, and design over automation.
That discipline doesn’t go away just because a machine can now autocomplete functions.
If anything, it becomes more important than ever.
My Takeaway
When that lead told me they’d deleted 200,000 lines of AI-generated code and everything still worked, I didn’t see it as a failure of the technology.
I saw it as a reminder that tools don’t replace engineering principles.
AI is a powerful assistant.
But trust it blindly, and it will quietly erode your system from the inside out.
The real productivity gain isn’t in the speed of generation it’s in the quality of judgment behind what stays and what gets deleted.
Use AI. Experiment with it.
But never forget: your codebase reflects your discipline, not your tools.
And discipline is still something only humans can provide.
Written by Ivan Turkovic; a technologist, Rubyist, and blockchain architect exploring how AI, code quality, and engineering culture shape the future of software.