The Hidden Cost of “Vibe Coding”: Why Generated Code Is the New Fast Fashion

There’s a growing unease among senior developers watching colleagues embrace AI-generated code with abandon. Not because the code doesn’t work—it often does, at first glance. The discomfort runs deeper: we’re watching an industry collectively outsource the thinking that makes software work in the real world.

The Core Insight

The author makes a provocative comparison that cuts to the heart of the issue: LLM-generated code is fast fashion for software. It looks acceptable at first glance, doesn’t hold up over time, is full of hidden holes, and is often trained on (read: ripped off from) other people’s work.

But here’s the crucial distinction that undermines the “Industrial Revolution” analogy AI boosters love: mechanization produces the same results each time. If something goes wrong, engineers can peer inside and diagnose the problem. LLM output is non-deterministic and opaque. There’s no utility in a mechanized process that produces something different every time, “often peppered with hallucinations.”

The “abstraction layer” argument fares no better. Yes, higher-level languages abstracted away assembly. But those abstractions didn’t remove the need for architectural thinking. You still need to reason about system design, critical paths, maintainability, browser support, accessibility, security, and performance. LLMs can’t reason about what system architecture should be because they cannot reason. They do not think.

“If we’re not thinking and they’re not thinking, that means nobody is thinking. Nothing good can come from software nobody has thought about.”

Why This Matters

The accountability dimension is particularly sobering in light of the Horizon scandal, where bugs in Post Office software led to innocent workers being prosecuted—and thirteen people killing themselves. When we outsource code generation to systems that can’t be held accountable, who bears responsibility when things go catastrophically wrong?

The erosion of code review quality compounds this problem. Reviewing a PR from a colleague carries implicit trust—someone has thought about this code. Generated PRs lack that epistemic foundation. When a company proudly shows off using Claude to generate PRs via Slack, they’re celebrating the removal of one pair of eyes from a two-person accountability chain.

The author’s framing of LLMs as “spicy autocomplete” is telling. In that limited role, AI assistance works fine. The danger emerges when developers believe they can “vibe code” their way to production-ready software. As one expert put it: only use agents for tasks you already know how to do, because it’s vital that you understand the output.

Key Takeaways

• Generated code is fast fashion: Looks fine initially, falls apart under scrutiny, environmental cost, trained on others’ work without consent
• Non-determinism kills the mechanization analogy: Machines in factories produce consistent output; LLMs don’t
• Abstraction ≠ abdication: Higher-level languages didn’t remove the need for architectural thinking; AI shouldn’t either
• Accountability requires understanding: The Horizon scandal shows what happens when software fails without anyone understanding why
• Four eyes good, two eyes bad: AI-generated PRs remove a crucial layer of shared context and accountability
• “Human centipede epistemology”: LLMs trained on bad human code produce bad reconstituted code, then future LLMs train on that output

Looking Ahead

The author’s position—”anti-hype, not anti-LLM”—points toward a sustainable middle ground. Using AI for prototypes and wireframes makes sense. Using it as “spicy autocomplete” for boilerplate is reasonable. The line gets drawn at outsourcing the actual thinking that transforms requirements into robust, maintainable systems.

The essay ends with a plea worth heeding: “Stop generating, start understanding, and remember what we enjoyed about doing this in the first place.” For those of us who got into software because we love the craft of building things, that’s not nostalgia. It’s a reminder that the interesting problems haven’t changed—and they still require human minds engaged at full capacity.

Based on analysis of “Stop generating, start thinking” from localghost.dev