Vibe Coding — Part 1: Behind the Vibe
AI-generated code feels almost too easy. You describe what you want; the model produces something runnable in seconds or minutes. The barrier between idea and implementation disappears. It’s so frictionless that building software can start to feel less like engineering and more like scrolling through short videos — that same dopamine-driven sense of instant gratification, and the illusion that complexity has evaporated.
How the Vibe Gets Formed
Generative models assemble code by pure probability. They draw on thousands of examples and blend patterns into something that looks familiar. This creates a smooth feeling of consistent formatting, confident naming, plausible abstractions. But underneath, the code is often structurally broken or logically inconsistent with the rest of your system.
I have a friend who literally vibe-codes between Mario Kart races — and not as a joke. He fires off fresh prompts to Claude while waiting for the next track to load. The surprising thing isn’t that resulting code compiles; and not even that my friend usually finishes in top 3; it’s that this workflow feels normal now. That’s the real commentary on vibe coding: it lowers the cognitive cost so much that you can generate “working software” in the gaps of a gaming session.
Once code lands in the repository, the dopamine rush ends and reality begins.
When the Vibe Is Enough
Vibe coding excels when consequences are low:
early prototypes
internal utilities with no uptime expectations
one-off scripts
boilerplate integrations
“I don’t care, I just need more cheap dopamine something that runs” situations
Here, speed is the primary value. The vibe doesn’t just help — it’s the optimal path.
When the Vibe Turns Against You
Problems appear when vibe-generated code touches the parts of a system where the hidden rules matter: concurrency, consistency, state transitions, performance critical paths, security boundaries. The generated code may pass basic tests, but it was not designed with operational stress or architectural invariants in mind.
The vibe gives you momentum — until the system pushes back.
Thinking in Terms of Impact and Probability
The question isn’t whether AI-generated code is “good”, “bad” or “ugly”. It’s whether the risk profile matches the consequences.
What is the impact if this part fails?
What is the probability that the model misunderstood what matters here?
When impact is high or probability is high, vibe coding isn’t speed — it’s debt. Such areas demand intentional design, not probabilistic assembly.
Behind the Vibe
Vibe coding isn’t magic and it isn’t sabotage — it’s a new form of convenience. Understanding what lies behind the vibe — the simplicity, the shortcuts, the psychological ease — is what allows engineering teams to use it without letting the system drift into chaos.
