You might have heard about the day the internet broke because of 11 lines of code.

In March 2016, a developer named Azer Koçulu unpublished his tiny npm package called left-pad after a dispute with npm and Kik. That single package — a 11-line function that adds padding to the left of a string — was a dependency for Babel, React, Webpack… basically everything. Within hours, builds at Facebook, Netflix, PayPal, and thousands of other companies started failing with errors like Cannot find module 'left-pad'.

The incident became legendary: The npm blog post, The Register’s coverage, and countless memes later, the takeaway was clear — modern software is a towering Jenga game built on invisible blocks maintained by strangers.

We fixed it quickly back then because senior engineers could open the source of left-pad, laugh at how trivial it was, copy-paste it into their own repo, and move on. They understood the plumbing.

Fast-forward to 2025. The new risk isn’t a missing 11-line utility anymore.

It’s 8,000 lines of perfectly formatted, well-documented, AI-generated code that nobody on your team has ever read.

Welcome to Vibe Coding

You know the flow:

“Give me a full-stack Next.js app with infinite scroll feed, real-time updates via Supabase, role-based auth, dark mode, and make it look like Twitter but minimalist.”

Thirty seconds later — boom — 12 files, 8,000 lines, tests included, Tailwind classes on point.

You run it. It works. You ship it. You celebrate with your co-founder over coffee.

That, my friends, is vibe coding.

It feels like magic. It is also the fastest way known to mankind to accrue catastrophic architectural debt.

Why This Is Scarier Than left-pad

With left-pad, the failure was: - External - Obvious (builds just stopped) - Easy to locate and fix

With vibe-coded applications, the failure is: - Internal and structural - Silent until you hit scale, a security audit, or an API deprecation - Almost impossible to debug because nobody knows why the service layer is split that way, why the batch size is 47, or why there’s a custom WebSocket fallback nobody asked for

You have replaced a dependency on one angry developer in Azerbaijan with a dependency on a stochastic parrot that optimized for “vibe” instead of correctness, performance, or maintainability.

The Day the Music Stops

Picture these real scenarios I’ve already seen in production (company names redacted to protect the guilty):

  1. A startup raised $8 M on a fully AI-generated codebase. Six months in, they needed to add multi-tenancy. The AI had used a global Prisma client with connection pooling that collapsed under 300 concurrent tenants. Nobody knew how to refactor it safely → full rewrite.
  2. A fintech company shipped an AI-generated payment flow. It passed all tests, looked beautiful… until someone noticed the encryption keys were being logged in plain text in development because the model copied a debug snippet from a 2022 blog post.
  3. A SaaS hit 50k MAU and started getting random 503s. The AI had implemented optimistic UI updates with local state reconciliation that worked perfectly at 100 users but turned into a race-condition nightmare at scale.

In every case, the fix wasn’t “ask the AI to patch it.” The fix was senior engineers spending weeks reverse-engineering code they never wrote, never reviewed, and never understood.

AI as Co-Pilot vs AI as Ghostwriter

I love AI coding tools. I use Cursor, GitHub Copilot, and Claude daily.

They are the best interns the world has ever seen: fast, tireless, polite, and surprisingly good at boilerplate.

But interns don’t design load-bearing structures.

Use AI to: - Scaffold CRUD - Write tests - Refactor repetitive code - Generate documentation - Suggest better algorithms

Do NOT use AI to: - Make high-level architectural decisions you can’t justify - Write core business logic you can’t explain line-by-line - Replace code review and ownership

The Rule I Live By Now

Every single line of code that goes to production must be read, understood, and explicitly approved by at least one human who can explain it without prompting an LLM.

No exceptions.

If your team can’t draw the data flow on a whiteboard, you don’t own the system — the model does.

Final Thought

The lesson of left-pad wasn’t “don’t depend on tiny packages.”
It was “know what you depend on, and never ship something you can’t fix when it breaks.”

In the age of AI, that lesson is more important than ever.

Use the machine. Ship faster than ever before.
But never, ever surrender the blueprints.

Because when the house collapses, “the AI wrote it” is not going to be a valid excuse to your customers, your investors, or your future self staring at a burning production server at 3 a.m.

Own your vibe.