Vibe Coding Isn’t the Problem. Not Understanding the Stack Is.

2026-06-20 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

The article discusses "vibe coding," a method of using AI to generate code by describing desired outcomes in plain language, and argues that the problem isn't the AI itself but a lack of understanding of the underlying system stack. The author, a systems engineer with two decades of experience, explains how their deep knowledge of infrastructure (servers, networks, databases, security layers) enables them to effectively use AI coding tools. They provide concrete examples where AI suggested problematic solutions, such as using Windows over Ubuntu for cost, MySQL over Postgres for maintainability, insecure login configurations, open network ports, and plain-text secrets. The author emphasizes that "full stack" development encompasses far more than just frontend and backend, including source control, APIs, authentication, databases, caches, object storage, queues, web servers, reverse proxies, and DNS. This foundational understanding allows developers to challenge AI suggestions and prevent critical production failures.

Key takeaway

For MLOps Engineers or AI Developers building applications with AI coding tools, you must prioritize deep understanding of the entire system stack, not just the application layer. Your ability to identify and correct AI-generated code that overlooks security, cost, or operational maintainability is paramount. Invest time in understanding infrastructure layers like networking, databases, and authentication policies to prevent critical production failures and ensure your AI-assisted projects are robust and secure.

Key insights

Understanding the full system stack, beyond application code, is critical for safe and effective AI-assisted development.

Principles

• The system stack extends far beyond frontend/backend.
• AI optimizes for "working" not "safe" or "maintainable."
• Knowledge of foundations enables challenging AI suggestions.

Method

Spend 30 minutes discussing the problem, constraints, and potential issues with the AI, then have the AI generate the best prompt for itself, and finally hand that prompt to the coding agent.

In practice

• Override AI suggestions for cost-effective OS choices.
• Store secrets securely, not in plain text.

Topics

• Vibe Coding
• System Stack
• AI Code Generation
• Infrastructure Security
• DevOps Practices
• Application Architecture

Best for: AI Architect, CTO, VP of Engineering/Data, AI Engineer, MLOps Engineer, DevOps Engineer

Related on AIssential

• You’re Shipping Code You Don’t Fully Own Anymore — AI-assisted "vibe coding" accelerates development but introduces hidden costs and systemic risks due to reduced understanding of generated infrastructure and code.
• What I Learned Vibe Coding Apps as a Non-Coder — AI-driven "Vibe Coding" empowers non-coders to prototype rapidly but introduces new complexities for production-grade applications.
• Vibe coding and agentic engineering are getting closer than I'd like — The increasing reliability of AI coding agents blurs the distinction between casual "vibe coding" and professional "agentic engineering."
• A new worst coder has entered the chat: vibe coding without code knowledge — Vibe coding tools enable rapid app creation but often produce insecure, unmaintainable code without developer oversight.
• How Sustainable is Vibe Coding? — AI's true value in coding lies in augmenting experienced developers, not replacing foundational understanding for novices.
• Vibe Coding Gets an Upgrade — AI development is converging towards multi-agent orchestration and event-driven automation, making "vibe coding" the new standard.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.