I Hate Vibe Coding

I hate vibe coding.

I hate how it's reportedly made developers significantly more productive.

I hate how fingers barely dance across keyboards before whole functions materialize like digital origami folding itself.

I hate how work is now measured not in lines of code but in ideas implemented.

For the uninitiated, "vibe coding" is the practice of programming with AI tools where you essentially transmit your intentions through a mix of natural language, code fragments, and contextual hints—and watch as your AI coding partner attempts to manifest your vision. It's almost telepathic. You think it; it builds it.

And I absolutely despise it.

Why I "Hate" It

I hate how imposter syndrome has potentially morphed into something new. The worry isn't about not knowing enough—it's about imagination not being vast enough. The bottleneck isn't technical knowledge; it's creative ambition.

I hate how development has shifted from agonizing over semicolons to debating the ethics of authentication flows. From fighting with merge conflicts to contemplating user psychology.

I hate that terminal histories now look like conversations with knowledgeable assistants rather than strings of copy-pasted Stack Overflow commands.

The Bandwagon is Getting Crowded

The adoption pattern appears to be widespread across the industry:

Senior engineers who once dismissed AI autocomplete now discuss prompt engineering techniques. They compare AI tools like they used to compare development environments.

Developers who previously relied solely on traditional editors are increasingly incorporating AI assistants into their workflows.

Technology leaders are publishing articles about AI-assisted development workflows and their potential impact on development velocity.

Systems programmers are beginning to use AI tools for generating boilerplate code while maintaining their focus on core functionality.

The Productivity Claims

Some companies report increases in development velocity when using AI coding tools. Anecdotal evidence suggests that developers at various experience levels are completing certain tasks more quickly than before.

Various studies and surveys have reported that teams using AI-assisted coding tools experience:

• Faster feature delivery
• Potential reduction in certain types of bugs
• Changes in developer satisfaction
• More consistent code patterns

These claims require careful evaluation, as the landscape is still evolving and long-term impacts remain to be seen.

The Adoption Process

The progression through AI coding tool adoption often follows a recognizable pattern:

1. Skepticism: "It's just sophisticated autocomplete."
2. Resistance: "These tools might impact fundamental learning."
3. Limited Testing: "I'll try it for documentation and simple tasks."
4. Concern: "My existing skills might need updating."
5. Integration: "These tools can generate substantial code with proper guidance."

Many developers report spending considerable time in the limited testing phase before broader adoption.

The Changing Skill Landscape

The skills emphasized in AI-assisted development appear to be shifting:

• Effective communication with AI tools
• Problem decomposition and specification
• System design and architecture
• Understanding user needs and business context

This transformation potentially requires developers to strengthen their ability to articulate problems clearly and think through comprehensive solutions.

The Reality Check

A key consideration about AI-assisted coding: it appears to amplify existing capabilities rather than replace fundamental knowledge.

Developers with limited understanding of programming principles may produce problematic code more quickly. Those with strong foundational knowledge can potentially build more sophisticated solutions at increased speed.

This dynamic may highlight the importance of solid engineering fundamentals in an AI-assisted development environment.

The Reluctant Acceptance

The truth about AI-assisted coding tools is nuanced. They represent a significant shift in how software development work gets done.

They can reduce time spent on repetitive tasks and boilerplate code.

They encourage focus on higher-level problem solving and system design.

They enable faster iteration on ideas and prototypes.

The software development field is experiencing changes where conceptual thinking and clear communication are becoming increasingly valuable alongside technical implementation skills.

For those observing this transformation from the sidelines, the tools and techniques continue to evolve rapidly. Understanding their capabilities and limitations will likely become an important part of modern software development.