Why “vibe coding” with AI assistants is failing developers

The rise of “vibe coding” – a flow-based approach to programming with AI agents – is facing critical scrutiny as developers begin to question its long-term productivity benefits. This reflective exploration of AI-assisted programming highlights the tension between the immediate satisfaction of rapid code generation and the hidden costs that can accumulate through overreliance on AI coding partners, suggesting that finding the right balance between AI assistance and traditional development practices remains an unsolved challenge for many programmers.

The big picture: “Vibe coding” describes the practice of programming in a flow state with AI assistance, prioritizing intuitive progress and the feeling of productivity over structured planning and rigorous testing.

• This approach involves heavy reliance on AI agents like Cline, Roo Code, and Cursor to generate code with minimal developer intervention.
• The author describes it as “watching an AI agent code for you, occasionally chiming in with a suggestion” rather than fully understanding the implementation details.

Why it’s failing: After two months of intensive vibe coding, the author identifies several fundamental problems that have led them to reconsider this approach.

• AI-generated code often requires significant rework due to context window limitations and a lack of comprehensive understanding.
• The practice can become a substantial time sink rather than the time-saver it initially promises to be.
• The financial costs of premium AI coding tools add up, particularly when they don’t deliver proportionate productivity gains.

The appeal factor: Despite its drawbacks, vibe coding remains attractive because it provides immediate satisfaction and the illusion of rapid progress.

• The dopamine hit from watching code materialize quickly creates a seductive feedback loop that feels productive.
• AI agents promise to anticipate developer needs and accelerate coding workflows, making the approach initially compelling.

Practical alternatives: The author suggests more balanced approaches to incorporating AI into development workflows.

• Free tools like Gemini Code Assist offer similar functionality without the financial burden of premium AI services.
• Open WebUI provides greater control and customizability while keeping costs relatively low for everyday programming tasks.

Reading between the lines: This personal account reflects a broader maturation in how developers are approaching AI coding assistants, moving from initial excitement to more nuanced, task-appropriate integration.

• The experience mirrors the typical technology adoption curve where users move past the “peak of inflated expectations” into a more realistic assessment of tools’ capabilities and limitations.
• The desire for both creative flow and technical rigor suggests developers are seeking AI tools that enhance rather than replace critical thinking in programming.