“Vibe coding” — when devs rely on AI to scaffold, guess, or freestyle parts of the codebase without deep understanding — is becoming a real thing.
Upside? Rapid prototyping.
Downside? Tech debt, security holes, and fragile spaghetti code.
In NZ, where SMEs often have smaller teams and less robust testing frameworks, vibe coding could cause headaches if not tempered with solid engineering practices.
“Use AI to speed you up — not to dumb you down.”
So here's the reality check.
- Potential for massive technical debt: Code generated without a comprehensive architectural plan or understanding of existing systems can become a tangled mess. This "spaghetti code" is challenging to maintain, update, and debug, which increases long-term costs and slows down future development.
- Security vulnerabilities: AI models, while powerful, may not inherently understand or prioritise security best practices. Vibe-coded solutions can introduce vulnerabilities (e.g., insecure direct object references, improper input validation, weak encryption) that are difficult to detect and exploit at scale, potentially leading to data breaches or system compromises.
- Fragile and unreliable Systems: Without a deep understanding of the underlying logic and dependencies, components built via vibe coding can be brittle. They might work in isolated tests but fail spectacularly under load, with unexpected inputs, or when integrated with other systems. This unreliability directly impacts user experience and business operations at scale.
- Poor performance and scalability issues: AI-generated code might be inefficient or not optimised for performance. It may not utilise appropriate data structures, algorithms, or concurrency patterns necessary for a system to handle a good number of users or transactions efficiently. This can lead to bottlenecks, slow response times, and ultimately, system failures when scaling up.
- Difficulty in debugging and troubleshooting: When issues arise in a vibe-coded system, pinpointing the root cause becomes a nightmare. The lack of clear design, inconsistent coding patterns, and potentially obscure AI-generated logic make debugging a time-consuming and frustrating process, especially in complex, distributed systems.
- Lack of readability and maintainability: Production systems require high readability and maintainability for teams to collaborate effectively and for future developers to understand and extend the codebase. Vibe-coded solutions often lack consistent style, clear comments, and logical structure, making them hard to read and impossible to maintain at scale.
- Challenges with compliance and auditing: For regulated industries or systems handling sensitive data, compliance and auditing are critical. Vibe-coded systems, due to their unpredictable nature and potential for hidden flaws, can make it exceedingly difficult to prove adherence to standards, regulations, or internal policies.
- Increased risk of "hallucinations" and logical errors: While AI is improving, it can still "hallucinate" or generate code that, while syntactically correct, contains subtle logical flaws or makes incorrect assumptions. In a production environment, these errors can have severe consequences, from incorrect calculations to system crashes.
- Loss of knowledge transfer and team understanding: When parts of a system are built without human developers fully grasping the generated code, it creates knowledge silos. This hinders team collaboration, peer reviews, and the ability to effectively onboard new team members or evolve the system over time.
In summary, while AI is an incredible accelerator, relying solely on "vibe coding" for production-ready, scalable systems is akin to building a skyscraper without blueprints or a solid structural foundation. It's fast at the outset, but is destined for significant challenges, high costs, and potential collapse in the long run. The true value lies in using AI as a tool to augment, not replace, human understanding and rigorous engineering practices.
