Before AI coding assistants, implementation cost acted as a natural filter on your backlog. A two-week feature got scrutinized. A two-hour feature often didn't. That asymmetry wasn't inefficiency — it was judgment.
Vibe coding removed it. Here's what the data shows, and how to rebuild it deliberately.
What the numbers say about dead code
The Stack Overflow 2025 Developer Survey found that 66% of developers identify AI solutions that are "almost right but not quite" as their top frustration. More relevantly, 45% say debugging AI-generated code is more time-consuming than they expected — often because the code itself shouldn't have been written in the first place.
A Fastly study of 791 professional developers (July 2025) found that 28% of developers frequently need to fix or edit AI-generated code enough that it offsets most of the time savings. And the 2025 DORA Report found that only 30% of developers report little or no trust in AI-generated code — meaning the majority are shipping code they can't fully vouch for.
Speed of execution is not the same as quality of decision. The research keeps confirming this.
What you actually lost
In a traditional workflow, the cost of implementation forced a question you probably didn't phrase explicitly but asked constantly: Is this worth my time?
That question filtered your backlog naturally. High-cost features needed a strong justification. Low-cost ones still had some friction — setting up the task, writing tests, reviewing the PR.
With vibe coding, the friction approaches zero. The question disappears with it.
The 2025 DORA Report names this dynamic directly. Their research found that AI adoption now positively correlates with software delivery throughput — you ship more. But it also continues to have a negative relationship with software delivery stability. The report's explanation: AI accelerates development but exposes weaknesses downstream. Without robust controls, increased volume leads to instability.
More features, shipped faster, breaking more things. That's the default outcome without deliberate prioritization.
A practical prioritization filter for AI-assisted development
The point isn't to slow down. It's to direct speed toward decisions that were actually worth making. Here's a minimal framework:
| Question | What it filters |
|---|---|
| Would I build this if it took 2 weeks? | Eliminates excitement-driven features |
| Who specifically asked for this? | Filters for user pull vs. builder push |
| What breaks if this doesn't exist? | Tests actual dependency vs. nice-to-have |
| What's the rollback plan? | Focuses on delivery stability, not just speed |
The DORA AI Capabilities Model identifies user-centric focus as one of the seven practices most critical to AI success — and one of the most overlooked. Teams without it experience negative impacts from AI adoption. The tool helps you build things faster. It does not help you decide what to build.
The scope problem
There's a secondary effect worth naming. The same DORA report found that the median developer spends two hours per day actively working with AI. At that pace, you're generating code faster than you can assimilate it. You become, as the report puts it, the owner of a system you don't fully understand.
That's manageable — it's structurally similar to managing a team. But it requires the same discipline a good manager applies: knowing what not to add, not just what to ship next.
The Stack Overflow 2025 survey reinforces this: only 3% of developers highly trust AI output, yet the majority use it daily. That gap between usage and trust is where dead code lives.
The bottom line
Vibe coding didn't break your judgment. It removed the forcing function that activated it. Rebuilding that filter deliberately — before you prompt, not after — is the practical response. The DORA data is consistent: AI amplifies what you already do. If you already had a rigorous prioritization practice, AI makes you more precise. If you didn't, it makes the gap larger, faster.
Sources
Top comments (1)
nice!