A dev just warned: AI agents are coding you into a corner
A veteran dev's viral essay warns AI coding agents create compounding errors faster than humans can fix. Three rules to stay in control.
Mario Zechner — the developer behind libGDX, a game development framework used by thousands of studios worldwide — just published an essay that's tearing through the developer community. The title: "Thoughts on slowing the fuck down."
His argument is blunt: AI coding agents are producing code so fast that developers are losing control of their own projects. And the consequences, he says, are already showing up in real products.
Simon Willison, one of the most influential voices in AI development, amplified the message with a stark summary: "You have zero idea what's going on because you delegated all your agency to your agents."
The Speed Trap Nobody Talks About
When you write code by hand, the slow pace forces understanding. Every line you type builds what developers call "mental context" — a map of how everything connects. It's like learning a city by walking through it, street by street.
AI coding agents blow up that map. Tools like Claude Code and Cursor can generate thousands of lines in minutes. That sounds incredible — until you realize no human can review that volume at speed.
The core problem: AI agents don't learn from their mistakes. A human developer who writes a bug once will usually avoid it next time. An AI agent will make the same error across 50 files in 5 minutes — and you won't notice until something breaks in production.
Zechner calls this "compounding errors without learning." Small mistakes that a developer would naturally catch pile up exponentially. What takes humans years of gradual tech debt, AI agents accomplish in weeks.
How Projects Get "Coded Into a Corner"
The essay references a growing pattern across the industry: companies that leaned heavily on AI agents and now can't maintain what was built. Zechner calls them "agentically coded into a corner" — meaning the AI-generated codebase became so tangled that rebuilding from scratch was easier than fixing it.
Here's why this happens, according to the essay:
1. Loss of oversight. When AI writes 2,000 lines in an afternoon, no one fully understands what was written. The developer becomes a manager of code they've never read.
2. Duplicated and inconsistent code. AI agents struggle to search entire codebases reliably. They often rewrite functions that already exist, creating multiple versions of the same logic in different files.
3. Cargo-cult architecture. Agents have seen millions of bad patterns in their training data and reproduce them without strategic thinking. The result: projects that look sophisticated but crumble under real-world pressure.
Mario Zechner isn't an AI skeptic — he's a veteran developer who created one of the most popular game frameworks on GitHub. His argument isn't "stop using AI." It's: "Use AI, but don't let it outrun your ability to understand what it built."
Three Rules That Could Save Your Next Project
Zechner's essay doesn't just diagnose the problem — it offers a concrete playbook. Here are his three rules, translated for anyone using AI tools to build:
Rule 1: Set a daily code limit.
Only generate as much AI code as you can realistically review that day. If you can review 200 lines carefully, generate 200 lines — not 2,000. The extra speed isn't worth it if you can't verify what was written.
Rule 2: Write the important decisions by hand.
Let AI handle the repetitive work — tests, boilerplate (standard template code), data formatting. But write the architecture yourself: how components connect, what the data models look like, where the system boundaries are. These decisions shape everything downstream.
Rule 3: Maintain understanding at all times.
Zechner's test: if you can't explain how a part of your codebase works without reading it first, you've already lost control. That's when bugs hide, features break, and rebuilds become necessary.
Why This Warning Matters Right Now
The essay lands at a critical moment. AI-assisted coding has exploded in the last year:
15.8 million commits generated by Claude Code alone in the last 90 days, across 844,000+ repositories (claudescode.dev)
114,785 new repositories received their first AI-generated commit in just the last 7 days
30.7 billion net lines of code added by AI tools — growing 8% week over week
Simon Willison, the creator of Datasette and one of AI's most-cited developers, agrees with Zechner's core thesis. But he adds a nuance: the solution isn't necessarily hand-writing all code. It's finding new discipline around speed versus understanding.
Willison's take: "Code generation is no longer the bottleneck in software development. Understanding is."
What You Should Do Today
If you're using AI tools like Claude Code, Cursor, or GitHub Copilot to write code — whether for a side project or at work — here's a practical checklist based on Zechner's essay:
Before your next coding session: Ask yourself — can I explain how my project works, right now, without opening the files?
Set a review budget: Decide how many AI-generated lines you'll accept today. Stick to it.
Touch the architecture yourself: Write the project structure, main function connections, and data flow by hand. Even if AI could do it faster.
If you've already lost the thread: Stop generating. Read through what exists. Refactor until you understand every piece. Then resume with tighter limits.
Zechner's essay isn't anti-AI. It's pro-thinking. The tools are powerful — but only if the person using them stays in the driver's seat.
Read the full essay: "Thoughts on slowing the fuck down" by Mario Zechner
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