Vibe Coding — Part 2: The Good, The Bad and The Ugly
The Good: Speed, Exploration, Momentum
Used well vibe coding accelerates teams through the friction of boilerplate and routine tasks. It produces working prototypes in minutes. It reduces activation energy for experimentation. When the consequences of failure are small, vibe coding is not only acceptable — it is the optimal choice.
In these safe zones the vibe is really good: quick wins, faster learning loops, lighter mental load.
The Bad: Hidden Fragility
But the same qualities that make vibe coding feel magical can also make it quietly dangerous. The model doesn’t reason about invariants, performance constraints, consistency models, or long term maintainability. It doesn’t know which parts of your architecture are load bearing. This is where the vibe becomes misleading: a surface level correctness masking deeper weaknesses. Everything works — until it suddenly doesn’t.
That’s the “bad”: code that fails far outside the developer’s field of view.
The Ugly: When the Vibe Breaks the System
And then there’s the part no one likes — the ugly failures. The failures that appear not during development, but at 3 am during a surge in traffic. The failures that come from a single missing retry strategy, a subtle concurrency flaw, an unsafe default, or a misinterpreted domain rule.
These failures resemble the sudden chaos of a classic standoff scene: AI-generated shortcuts, undocumented assumptions, and real world conditions. And there is only one inevitable outcome. And just like in the old Western, nobody walks away clean.
This is where vibe coding crosses the line from “risky” to “unacceptable”: silent defects that activate only under stress.
The Real Engineering Choice
The trio of the good, the bad, and the ugly is not a moral alignment chart — it’s a tension that every system eventually exposes. Vibe coding gives you speed, but systems demand clarity, structure, and predictability. Somewhere between those forces, an engineering choice must be made.
The real decision is not “trust AI or don’t”. It’s “which parts of my system can absorb uncertainty — and which absolutely cannot?”
Some layers tolerate shortcuts: UI glue, scaffolding, prototypes, low stakes helpers. Others react badly to even tiny deviations: state machines, payment flows, security gates, anything where failure ripples outward.
The craft is in mapping your system’s pressure points — understanding where looseness accelerates learning, and where looseness becomes structural instability.
The Way Forward
Vibe coding is not inherently good or dangerous — it’s reactive. It mirrors the environment it enters.
In mature teams with clear boundaries, observability, conventions, and guardrails, vibe coding becomes a force multiplier. It speeds iteration, reduces cognitive load, and clears space for creative thinking.
Part 2 is not about sounding the alarm. It’s about understanding that AI doesn’t change what makes software stable — it only changes how quickly a team can move towards stability or away from it.
