I keep hearing the same complaint about AI pair programmers: “It works, but it’s gibberish.” The functions compile, the tests pass, yet the repo slowly fills with mystery helpers and odd shortcuts. So what counts more: how the code reads or what it delivers? Let’s walk through both angles and pick a stance.

Why the model still writes nonsenseLink to heading

My repos are full of helpers named things like handleItRealQuick because I let models scaffold at 2× speed. When I dig into the diff, the same reasons pop up:

• Remix mode. Models mash bits of code that often appear together, so you get cargo-cult layers unless you steer them hard.
• Thin context. I rarely paste more than a slice of the repo, so the model fills gaps with made-up abstractions.
• No pain loop. I learn from on-call nights; the model never does. Without tight review gates, it keeps repeating the same oddities.

So yes, “gibberish” fits. The better question is when it’s a blocker versus just background noise.

When the mess costs more than it helpsLink to heading

• Rotating ownership. If another team inherits the module next quarter, I want boring, obvious code—not AI poems.
• Audits. Regulators don’t care that “tests passed.” Someone has to explain what’s happening line by line.
• Shared libraries. Core utilities spread everywhere. If they’re weird, every feature inherits the weirdness.
• On-call nights. At 3 a.m., clear intent saves more time than cleverness ever will.

In these spots I slow down, regenerate, or just rewrite it myself.

When I let the gibberish rideLink to heading

• Spikes and prototypes. I care more about learning fast than naming things perfectly.
• Migration shims. If the code dies after a cutover, I’ll tolerate the mess.
• Tiny teams. Sometimes shipping something slightly ugly keeps the roadmap moving.
• Agent-only glue. If humans never touch it and the pipeline can regen it safely, entropy is fine.

In those cases the “burn it down, it’s ugly” take just slows momentum.

My quick gut checkLink to heading

I run through five fast questions before merging anything AI touched:

1. Will someone read this monthly or once a year?
2. Does it outlive the current milestone?
3. Who gets hurt if it misbehaves?
4. Could I explain the abstraction in one paragraph?
5. Would another prompt + style guide get me a cleaner version in under an hour?

If the answers skew low risk, I merge. If they lean high risk, I regenerate or rewrite.

Where I standLink to heading

Gibberish matters when it hides risk. Functionality is the ticket in; clarity keeps that feature cheap to own. I’m aiming for practical balance: ship the code that meets the risk bar, flag the spots where clear language is required, and keep teaching the models what “readable here” looks like. Wins come from knowing when mess is acceptable and when it isn’t—not from banning AI slang outright.