A new service category is quietly emerging in tech: Vibe Coding cleanup. What started as LinkedIn jokes about “fixing AI messes” has become a real business opportunity. The harsh reality nobody wants to admit: most AI-generated code is production-unready, and companies are desperately hiring specialists to fix it before their technical debt spirals out of control.
The vibe coding explosion
When Andrej Karpathy coined “vibe coding” in early 2025, he perfectly captured how developers now work: chatting with AI to generate entire functions instead of writing them. The approach promises 10x productivity gains through natural language programming. GitHub reports that 92% of developers now use AI coding tools, with Copilot alone generating billions of lines of code monthly.
But there’s a problem nobody talks about at conferences. GitClear’s analysis of 150 million lines of code reveals AI assistance correlates with 41% more code churn - code that gets reverted or rewritten within two weeks. Stanford researchers found that developers using AI assistants produce significantly less secure code while believing it’s more secure. The tools amplify bad practices: no input validation, outdated dependencies, and architectural decisions that make senior engineers weep.
The cleanup economy is real
404 Media’s investigation reveals developers are building entire careers around fixing AI-generated code. Hamid Siddiqi manages 15-20 cleanup projects simultaneously, charging premium rates to untangle what he calls “AI spaghetti” - inconsistent interfaces, redundant functions, and business logic that makes no sense. Software consultancy Ulam Labs now advertises “Vibe Coding cleanup” as a core service.
The demand is so high that VibeCodeFixers.com launched as a dedicated marketplace. Within weeks, 300 specialists signed up and dozens of projects were matched. Founder Swatantra Sohni describes a typical client: “They burned through $5,000 in OpenAI credits, have a half-working prototype they’re emotionally attached to, and need it production-ready yesterday.” The Pragmatic Engineer reports similar patterns across Silicon Valley startups.
Why AI code fails at scale
The fundamental issue isn’t that AI writes bad code - it’s that it writes locally optimized code without understanding system context. Stack Overflow’s analysis shows AI excels at small, isolated tasks but fails at architectural decisions. Every prompt creates technical debt: inconsistent patterns, duplicated logic, and security holes that automated scanners miss.
Computer Weekly reports that 40% of AI-generated code contains security vulnerabilities. The tools leak secrets into code, suggest deprecated libraries, and create race conditions that only appear under load. Worse, developers often don’t understand the generated code well enough to spot these issues. Martin Fowler warns this creates “competency debt” - teams lose the ability to maintain their own systems.
The market opportunity
The Vibe Coding cleanup market is growing rapidly, though exact numbers are hard to pin down. What we know: Gartner predicts 75% of enterprise software engineers will use AI code assistants by 2028. If even a fraction of those projects need cleanup - and current data suggests most will - we’re looking at a massive emerging market.
The economics are compelling. Startups save weeks getting to MVP with Vibe Coding, then spend comparable time and budget on cleanup. But that’s still faster than traditional development. The specialists who can efficiently refactor AI messes command $200-400/hour rates. Some are building productized services: fixed-price cleanup packages, AI code audits, and “vibe-to-production” pipelines.
ThoughtWorks reports 60% of their AI-assisted projects require significant refactoring before production. Multiple consultancies are now hiring specifically for “AI code remediation” roles. The market is real, growing, and largely untapped.
What this means for engineering
We’re witnessing a fundamental shift in how software gets built. AI handles the initial implementation, humans handle architecture, testing, and cleanup. It’s not the future we expected, but it’s the one we’re getting.
Gergely Orosz argues AI tools are “expensive junior engineers” - they write lots of code quickly but need constant supervision. The difference is that AI juniors never become seniors. They’ll always need cleanup specialists.
This creates interesting career paths. Junior developers who master Vibe Coding cleanup can command senior salaries within two years. Senior engineers who understand both AI capabilities and limitations become invaluable. Companies that build robust cleanup processes gain competitive advantage.
Our stance
At Donado Labs, we’ve cleaned up enough vibe-coded disasters to recognize the pattern. AI acceleration works, but only with professional cleanup built into the process. We use AI for prototyping and routine tasks, but architecture and critical logic remain human-written. Our “Vibe to Production” service takes AI prototypes and makes them enterprise-ready: proper testing, security hardening, and documentation that won’t make your successor cry.
The companies succeeding with AI coding aren’t the ones using it most - they’re the ones using it smartly. They prototype with AI, then invest in cleanup before technical debt compounds. They treat Vibe Coding like any other tool: powerful but dangerous without expertise.
Next time someone claims AI will replace programmers, ask them who’s going to clean up the code. That’s where the real opportunity lies.