This world of artificial intelligence has witnessed a tremendous shift in software development. Through code completion to fully autonomous AI, which builds, runs, and deploys software itself, development has never been done so quickly. In the face of this change, a new buzzword among techies has started doing the rounds: “Vibe coding.”
Vibe coding may sound casual, but it’s a serious change in the way software development occurs. And with any change in software development comes the risks involved in using such a method in a business.
This article, written by Mubashhir Pawle, discusses the meaning of vibe coding, why it is popular, and the five most dangerous pitfalls you must not ignore.
What Is Vibe Coding?
Vibe coding refers to a development approach where AI tools generate, modify, and sometimes deploy code with minimal human intervention. Instead of carefully designing and architecting systems line by line, developers “go with the flow” prompting AI systems to produce features, integrations, and even infrastructure.
Recent industry trends show why this approach is growing:
- Developers now use AI coding assistants in some capacity.
- Productivity gains for AI development tools are predicted at anywhere between 20% and 55%, depending on the complexity of the task.
But productivity does not always equal reliability. AI models generate code based on patterns and not due to real understanding. They may not understand business logic, long-term architecture concerns, or security implications.
That is where the risks begin.
Real-World Trend: AI Writing Code at Major Tech Companies
The emergence of vibe coding is far from being theoretical, and tech companies around the world are embracing it already. For instance, a recent announcement by Coinbase, which is considered a leading cryptocurrency exchange platform, noted that approximately 40% of its code is presently generated through AI platforms, and its intention is to increase that ratio to over 50% in due course. The CEO noted that although code generated by AI needs review and responsible use, not all parts of its business can rely on AI for generating code.
This, in turn, brings out the general industry trend that there is an integration of AI into the development of software codes. It also brings out an important point on the concept of vibe coding: even though there is an integration of a large percentage of codes by AI, it still needs to be governed.
1. Vanishing Database – When AI Resets Critical Data
One of the most alarming risks in vibe coding is unintentional data loss.
AI agents can:
- Reinitialize databases during deployments.
- Replace migration scripts incorrectly.
Drop or overwrite production tables when “cleaning up” schema conflicts.
Since AI systems may try to solve problems on their own, they may perform harmful operations without fully understanding the implications. In environments where deployment safeguards are weak, this can result in complete database resets.
For businesses that deal with financial data or healthcare, for example, this is not a programming mistake, but a potential issue of compliance and law.
Preventive measures include:
- Enforcing strict staging-to-production workflows.
- Restricting AI agents from direct database manipulation.
- Maintaining Automated Backups and Rollback Strategies.
Development may speed up with the help of AI, but human responsibility will still be needed in the end
2. Open Wallet Mistake – Uncontrolled AI Costs
AI services operate on usage-based pricing models. Every API call, prompt execution, model training iteration, and automated workflow consumes credits.
In vibe coding environments where agents autonomously:
- Run repeated test cycles,
- Regenerate code versions,
- Execute background tasks,
- Interact with cloud infrastructure,
- Costs can escalate rapidly.
Without usage caps or monitoring systems in place, companies may encounter unexpected billing spikes. This is particularly risky for startups and SMEs operating within tight budgets.
The “open wallet” problem occurs when:
- Budget limits are not configured.
- Usage alerts are not enabled.
- AI agents are granted unrestricted access to paid APIs.
Financial governance must evolve alongside technical governance. AI should be treated as a metered resource, not an unlimited utility.
3. Goldfish Memory – Breaking Existing Functionality
Also, AI models don’t maintain deep and long-term contextual memory of entire projects. Though they can handle large blocks of code, they sometimes struggle with intricate and old code bases that have been developed over time.
When new features are introduced via AI prompts:
- Older functions may be overwritten.
- Dependencies may be broken.
- Edge cases may be ignored.
- Architectural consistency may deteriorate.
Developers frequently describe this as the “goldfish memory” problem, the AI focuses on the latest prompt and loses awareness of previous design decisions.
This results in:
- Increased debugging time.
- Growing technical debt.
- Inconsistent coding standards.
- Fragile codebases.
Organizations must ensure:
- Code reviews remain mandatory.
- Regression testing is automated.
- The code generated by AI is considered a draft, not final output.
AI increases productivity, but it does not replace engineering discipline.
4. White Screen of Death – Ignoring User Experience
AI tools are optimized for functionality, not usability.
They can generate working backend logic and front-end components, but often overlook critical UX elements such as:
- Proper loading states,
- Error handling flows,
- Network failure recovery,
- Performance optimization,
- Accessibility compliance.
The result is applications that technically “work” but deliver poor user experiences including blank pages, unresponsive interfaces, or incomplete states commonly referred to as the “white screen of death.”
User experience requires empathy, product thinking, and contextual understanding. AI does not naturally account for real user behavior unless explicitly guided.
To mitigate this risk:
- Define UI/UX standards clearly.
- Incorporate design reviews.
- Implement front-end testing frameworks.
- Validate performance under real-world scenarios.
Software quality is measured not only by logic correctness but by user satisfaction.
5. Keys Exposure – Security Breaches in Plain Sight
One of the most critical security risks in vibe coding is improper handling of credentials.
AI-generated code frequently includes:
- Hard-coded API keys,
- Embedded tokens in front-end code,
- Credentials stored in configuration files,
- Secrets accidentally committed to version control.
This happens because AI models prioritize making code functional quickly. They may not distinguish between secure production practices and rapid prototyping shortcuts.
Credential exposure can lead to:
- Data breaches,
- Cloud infrastructure misuse,
- Financial loss,
- Reputation damage.
Best practices include:
- Storing secrets in environment variables.
- Using secure key vault systems.
- Running automated secret scanning tools.
- Strict security checks for CI/CD.
Security should never be an afterthought in AI-assisted development.
Additional Risks Businesses Should Consider
Beyond the five core issues, vibe coding raises other organizational issues:
- More technical debt due to inconsistent code quality.
- Compliance risks in regulated industries.
- Over-reliance on AI leading to a reduction of core engineering skills
- Intellectual property ambiguity of AI-generated code.
- Difficulty in maintaining large codebases generated by AI.
Although AI technology has the capacity to increase speed, unmanaged acceleration tends to lead to complexity.
Is Vibe Coding the Future?
AI-driven development is not a temporary trend but the future. It is increasingly becoming ingrained in the workflows of modern engineering practices across different industries. Not only are large enterprises adopting AI coding assistants at scale, but startups also depend on them to get to market faster.
However, responsible AI adoption requires:
- Clear governance policies.
- Technical oversight.
- Security-first development culture.
- Continuous monitoring and then evaluation.
The future of software development is not about replacing engineers with AI, but about empowering engineers with AI without even losing control.
Final Thoughts
Vibe coding is a major breakthrough in the development of applications, and it has the potential to speed up, innovation, remove mundane tasks, and boosts productivity. It is, however, has the potential to cause data loss, cost escalation, system breakdown, and security risks if it is not controlled effectively.
Companies need to have a strategy for AI-assisted development.
At Eiosys, we believe that intelligent automation and engineering discipline are not mutually exclusive concepts. AI is designed to support and enhance expertise, and not to replace accountability.
If your company is looking to adopt AI-powered development, the trick is to find out the right balance between the power of automation and the rigor of professional software development.
That’s how you can transform vibe coding from a threat to a strength.
