Do You Need Vibe Code Cleanup Services?

TL;DR Vibe coding, i.e. blindly accepting AI-generated code without review, has created a new profession: vibe code cleanup specialists who fix the security vulnerabilities, performance issues, and maintainability problems that plague unvetted AI output. While 85% of developers use AI coding tools, 78% don't do vibe coding because they understand the critical difference: AI-assisted development maintains professional standards throughout the process, while vibe coding delegates everything to AI and requires expensive cleanup afterward.

One of the biggest questions regarding AI is whether it will create more jobs than it replaces. So far, we can confidently say that it has created at least one new job: vibe code cleanup specialist.

According to one analysis, the AI coding tools market reached USD 6.04 billion in 2024, and is expected to reach USD 37.34 billion by 2032. However, that data already seems to be outdated - in 2025, the value of vibe-coding platforms alone (ignoring other types of AI coding tools) has exceeded USD 36 billion.

It’s not surprising. Take a scroll through LinkedIn on any given day and all the popular posts are about building apps or workflows with AI. But if building software with AI is so easy, why does anyone need vibe code cleanup services? 

Because if you want to vibe code effectively, you need to know what you can delegate. When non-technical entrepreneurs or business professionals let AI take charge in designing and developing a product, they just don’t know how many wrong assumptions and mistakes the AI makes in the process.

Even software developers that know how to build products but aren’t proficient at AI-assisted coding yet, might find that AI takes them down a path they don’t know how to come back from. 

And that’s when they start looking for vibe code cleanup services.

When is Vibe Coding Not Enough?

When you build your first working piece of software with a vibe coding tool, it feels like magic. It usually takes a while for issues to arise. Take the example of Josh Andersen, an engineer who took up the challenge of building a product 100% with AI:

“I got the product launched. It worked. I was proud of what I’d created. Then came the moment that validated every concern in that MIT study: I needed to make a small change and realized I wasn’t confident I could do it. My own product, built under my direction, and I’d lost confidence in my ability to modify it.”

Josh is a skilled engineer with nearly 30 years of experience in the industry, and he still had a hard time making changes in a piece of software he vibe-coded. When you delegate 100% of product design and development to an AI tool, you’re no longer in control of what you’re building.

The main issue is that software development projects have always been fraught with uncertainty. Even a small change in business or user requirements during a project can result in new engineering challenges that were impossible to predict in the planning phase. 

A recent study has outlined 11 different causes of uncertainty in software projects:

• Market uncertainty - inaccurate predictions of user needs and market conditions result in products that nobody wants to use, which is why agile, incremental development is preferred

• Technological uncertainty - using cutting-edge technology can create problems in later stages of a project, when unexpected security or integration challenges arise

• Environmental uncertainty - suppliers, competitors, governments, or stakeholders might do something that introduces new difficulties in the project

• Socio-human uncertainty - differences between team members or bad interpersonal dynamics leading to disruptions in collaboration

• Estimation inaccuracies - if scope, timeline, or budget is underestimated during planning, unexpected time constraints and financial overruns will appear later on 

• Development complexity - the wrong choice of technology, tools, or methodologies will lead to unpredictable challenges during development

• Testing complexity - a suboptimal strategy for finding and resolving bugs can prevent a product from going to market quickly

• Maintenance and support challenges - if project documentation from earlier stages is lacking, maintenance and support issues can be needlessly costly and time-consuming

• Human capital - the wrong mix of skills and efficiency levels of project team members can slow down the project and results in sub-par quality

• Inter-project dependencies - uncertainties can arise when multiple teams are working on a product without proper alignment

• Communication gaps - misunderstanding within teams or across departments can create issues that disrupt project execution in a way that’s difficult to diagnose 

Vibe-coding tools don’t magically remove all of these uncertainties. They help software developers execute faster, and enable non-technical professionals to go from idea to prototype without having to engage developers or learn to code themselves. 

But the way that professional engineers and people with limited (or none at all) coding experience use AI tools is different. This creates a gap in the market that is now being filled by vibe code cleanup specialists.

Why Software Developers Don’t (Fully) Trust AI Coding Tools

According to the 2025 JetBrains survey of 24,534 developers across 194 countries:

• 85% of developers regularly use AI tools for coding and development

• 90% of them save at least an hour per week, and 20% save 8 hours or more

• 68% expect AI proficiency to become a requirement in their jobs

But they’re not going all-in and delegating everything to AI. They find use cases where AI tools are useful and add value, like:

• Writing boilerplate, repetitive code

• Searching for development-related information on the internet

• Converting code to other languages

• Writing code comments or code documentation

• Summarizing recent code changes

Thanks to their experience in building software without AI, they can identify the shortcomings of AI coding tools. Their main concerns are:

• The inconsistent quality of AI-generated code

• AI tools’ limited understanding of complex code and logic

• Privacy and security risks

• The potential negative impact on their own coding and development skills

• AI’s lack of context awareness

The 2025 Stack Overflow Developer Survey goes even deeper on the limitations of AI coding tools:

• Growing dislike - positive sentiment for AI tools has actually decreased, from 70% in 2024 to 60% in 2025.

• Low trust - only 3.1% developers have high trust for AI tools, whereas 19.6% highly distrust them, 26.1% somewhat distrust them, and 29.6% have limited trust for them.

• Inability to handle complex tasks - 25% of developers view AI tools as ‘good, but not great at handling complex tasks,’ 22% say they’re ‘bad at handling complex tasks,’ and 16.8% don’t use them for complex tasks at all.

• Refusal to use AI for critical tasks - developers don’t want to use AI for high-responsibility tasks, like deployment and monitoring (76% don't plan to) and project planning (69% don't plan to).

• Frustrating performance - 66% of developers are annoyed by ‘AI solutions that are almost right, but not quite,’ and 45% report that ‘debugging AI-generated code is more time-consuming.’

Most importantly, almost 78% of developers say they don’t do vibe-coding at all. But isn’t vibe-coding the same as AI-assisted coding? It couldn’t be more different.

Difference Between Vibe Coding and AI-assisted Coding 

In February, 2025, Andrej Karpathy (OpenAI co-founder and former Tesla AI director) fired off a category-defining tweet:

"There's a new kind of coding I call 'vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists."

The tweet exploded. By March 2025, Merriam-Webster added "vibe coding" to its dictionary as a trending slang term. The media picked up on it and ran stories about it. Soon, social media began filling with memes about "vibe coding cleanup specialists."

Simon Willison, a thoughtful voice in the debate, draws a critical distinction: 

"If an LLM wrote the code for you, and you then reviewed it, tested it thoroughly and made sure you could explain how it works to someone else that’s not vibe coding, it’s software development."

The difference is everything. Vibe coding means accepting AI output blindly, so vibe code cleanup is necessary to bring it up to professional standards after the fact. AI-assisted software development is making sure that professional standards are maintained from beginning to end, and using AI to speed up execution.

What Problems Can Vibe-Coding Cause?

AI can hallucinate functions and variables that don't exist. It loses context as applications grow, forgetting earlier instructions. It takes the quick route to solving problems instead of the correct route. It assumes happy paths, omitting error handling for edge cases. AI-generated code contains systematic security vulnerabilities, leaving databases open to SQL injection, ignoring input validation, or leaving in hardcoded API keys.

Databricks' AI Red Team documented specific catastrophic vulnerabilities, like pickle deserialization allowing arbitrary code execution, GGUF binary parser with unsafe memcpy calls enabling out-of-bounds memory access, and network code in a game with dangerous patterns. 

In an August 2025 survey of 18 CTOs, 16 reported production disasters caused specifically by AI-generated code. They noted that vibe coding puts critical software qualities at risk: security, clarity, maintainability, and team knowledge. Here are a few of the situations they brought up:

• A performance disaster caused by an AI-generated database query that aced the tests, but destroyed performance in production because it wasn’t optimized for scale. 

• A security lapse in an AI-written authentication module, where users with deactivated accounts could still access admin tools.

• A maintainability and complexity challenge that happened when developers tried to extend a feature that was 100% vibe-coded, ultimately choosing to rewrite everything from scratch.

Still, AI isn’t going away, and it’s changing developers’ jobs. As one engineering leader put it:

"I am, personally, reviewing 10 times the amount of code I was last year, but I can't just run at a 10x pace to make sure all this code generated by AIs fits our standards."

What Are Vibe Code Cleanup Services?

Traditional code refactoring improves human-written code incrementally. Developers understand the original intent, identify code smells, apply design patterns, and reduce technical debt during normal development cycles. 

Vibe code cleanup is fundamentally different. It's archaeological work on code the developer didn't write and may not understand, requiring systematic fixing of AI-specific pathologies. Vibe code cleanup specialists face unique challenges.

In an interview with 404 Media, Hamid Siddiqi, a vibe code cleanup developer promoting his services on Fiverr, said he had been doing this work since late 2023 - before the term even existed. 

"I started fixing vibe-coded projects because I noticed a growing number of developers and small teams struggling to refine AI-generated code that was functional but lacked the polish needed to align with their vision." 

As noted in a recent TechCrunch report on vibe code cleanup, “AI-generated code and vibe-coding platforms are useful in many situations,” but “human review is essential before building a business on it.”

One developer TechCrunch interviewed, Feridoon Malekzadeh, offered perhaps the most visceral description: 

"Vibe coding is akin to hiring your stubborn, insolent teenager to help you do something. You have to ask them 15 times. In the end, they do some of what you asked, some stuff you didn't ask for, and they break a bunch of things along the way." 

Despite this, he maintains AI coders help him accomplish more. He spends 50% of his time writing requirements, 10-20% vibe coding, and 30-40% vibe fixing.

Vibe Code Cleanup Best Practices

Chris Rickard, Founder at Userdoc, offers helpful general advice for teams implementing AI-assisted workflows: 

• Anticipate complexity - predict complex challenges that can arise as the project develops and evolves.

• Consider external help - don't hesitate to seek external assistance if in-house AI capabilities are saturated, especially in urgent situations where performance is compromised.

• Vet third-party agencies - have a plan for vetting' vibe coding repair agencies,’ focusing on their experience, capability to resolve similar issues, and their approach towards transforming such challenges into opportunities.

• Regular audits - incorporate regular audits of your AI project to recognize potential pitfalls early and determine whether external intervention might be necessary.

• Develop an escalation plan - formulate an escalation plan to engage external specialists for complex AI problems, to ensure minimal disruption to the project timeline and maintain quality standards.

If you’re looking for more knowledge, check out the Vibe Coding Framework project. It’s a collection of vibe coding best practices that includes: 

• Prompt Engineering System

• Verification Protocols

• Security Toolkit

• Documentation Generator

• Refactoring Tools

• Team Collaboration

Their R.E.F.A.C.T. Framework provides a comprehensive cleanup methodology: 

• Recognize patterns the AI attempted to implement

• Extract components into modular, reusable pieces

• Format for readability with proper naming and documentation

• Address edge cases with validation and error handling

• Confirm functionality through comprehensive testing

• Tune performance by optimizing queries and reducing overhead

Thanks to the proliferance of vibe coding, security scanning tools have grown in popularity. AquilaX audits AI-generated code for backdoors and insecure configurations. Snyk Code provides real-time vulnerability scanning with auto-fixing capabilities. SonarQube implements quality gates that AI output must pass.

Language-specific patterns for vibe-coding have emerged, like converting promise chains to async/await in JavaScript, or breaking down large functions in Python. The patterns acknowledge what AI does poorly, like proper error handling, consistent architecture, and security considerations, and provide systematic fixes.

All in all, vibe code cleanup has become a fully fledged job, separate from other specializations in the software development industry.

The Future of Vibe Code Cleanup

There seem to be at least 2 scenarios for how the future of vibe code cleanup will play out.

One, AI could improve so quickly that soon the output of vibe-coding tools won’t require any cleanup. Or, If AI capabilities remain at their current level, vibe code cleanup specialists could become a staple of high-performing product development teams.

The future of software development likely involves this hybrid model becoming standard practice. AI handles generation, humans handle judgment. AI writes implementations, humans design systems. AI produces code quickly, cleanup specialists make it sustainable. 

Not the "no engineers required" fantasy some envisioned, but a pragmatic evolution where different capabilities complement each other.

If you're looking for support with AI, vibe coding, or product development in general, Monterail is here to help.