"Clean code" has long been a badge of honor in software engineering, a discipline of readability, maintainability, and intent. But as AI increasingly steps into the role of code generator, reviewer, and even maintainer, it's time to ask a provocative question:
Is clean code still necessary, or has it become a nostalgic relic of pre-AI programming?
The Original Purpose of Clean Code
Clean code emerged from a need for clarity and maintainability among developers and software engineers. Codebases were read far more often than they were written. Principles like SOLID, meaningful naming, and concise abstractions were created to make the experience of reading and maintaining code manageable.
But what happens when the primary reader of code is no longer human?
The Case Against Clean Code in the AI Era
AI doesn't care how clean your code is: Large language models can parse spaghetti code just as easily as pristine functions. Indentation and naming conventions are irrelevant to a transformer model. In fact, some AI models have been shown to perform better with more verbose, explicit code that includes redundant checks and comments, as it provides more context for understanding the intent. The traditional emphasis on human-readable code might be less relevant when the primary consumer of the code is an AI system.
Speed trumps style: In a startup, MVP, or experimental context, obsessing over style might slow down progress unnecessarily. When AI can generate, test, and deploy code in minutes, the traditional cycle of code review and refactoring becomes a bottleneck. Consider a scenario where you need to prototype a new feature: AI can generate working code faster than a human can write clean code, and the prototype can be refined later if needed. This shift in priorities challenges the traditional "write it right the first time" mentality.
Machines maintain machines: If AI can not only write but also refactor and optimize code, why should a human waste time polishing it? Modern AI tools can:
- Automatically detect and fix code smells
- Suggest performance optimizations
- Generate comprehensive test coverage
- Refactor code to follow best practices
- Update dependencies and security patches
Code as an intermediate product: With declarative tools, visual programming, and prompt-driven workflows rising, traditional code may become just one form of instruction. We're already seeing this shift with no-code/low-code platforms that generate code behind the scenes, AI-powered code generation from natural language descriptions, and visual programming interfaces that compile to code. In this new paradigm, the emphasis shifts from writing clean code to writing clear specifications and prompts that AI can interpret and implement.
The rise of "good enough" code: In many cases, AI-generated code that works reliably is more valuable than meticulously crafted clean code that takes longer to produce. This is especially true in rapid prototyping, one-off scripts, temporary features, or legacy system maintenance where complete refactoring isn't feasible. The traditional trade-off between speed and quality is being redefined by AI's ability to generate functional code quickly.
AI's different optimization priorities: While humans optimize for readability and maintainability, AI might optimize for:
- Performance and resource efficiency
- Security and vulnerability prevention
- Compatibility with specific frameworks or platforms
- Ease of automated testing and deployment
Why Clean Code Still Matters
Cognitive clarity still counts: Even with AI, most teams aren't fully autonomous yet. People still onboard, debug, and learn from code. Clean code serves as a form of documentation and knowledge transfer, making it easier for new team members to understand the system, developers to debug issues in production, and teams to collaborate effectively across different time zones. This human element remains crucial, especially in complex systems where understanding the "why" behind the code is as important as the "how."
Tooling depends on good structure: The modern development ecosystem relies heavily on automated tools that assume reasonable code quality and organization. Clean code enables more accurate static analysis, better performance of automated testing tools, and more reliable dependency management. These tools are becoming increasingly sophisticated, but they still require a foundation of well-structured code to function effectively. Without this foundation, even the most advanced AI tools can struggle to provide meaningful assistance.
Clean code promotes better thinking: The process of writing clean code forces developers to understand the problem deeply and consider edge cases they might otherwise miss. Well-structured code makes architectural patterns more visible, while clear abstractions help identify potential design flaws. This isn't just about aesthetics, it's about creating a codebase that accurately reflects the problem domain and makes it easier to reason about the system's behavior.
Ethical and regulatory implications: In industries like healthcare, finance, or defense, human readability remains critical for audits and safety. Clean code is essential for:
- Compliance with industry regulations
- Security audits and penetration testing
- Legal documentation and liability protection
- Quality assurance and certification processes
AI training and improvement: Clean code serves as better training data for AI systems. Well-structured code helps AI learn better patterns, while consistent naming conventions improve AI's understanding of the codebase. This creates a virtuous cycle: clean code helps AI generate better code, which in turn helps maintain code quality. The relationship between clean code and AI is symbiotic, not adversarial.
Long-term maintainability: Even with AI assistance, some code will need to live for years. Clean code is easier to update and modernize, while well-documented code survives team changes better. This is particularly important in enterprise environments where systems may need to operate for decades. The initial investment in clean code pays dividends in reduced maintenance costs and easier system evolution.
The Hybrid Reality
We're entering an era of augmented development, where humans and machines collaborate in new ways. Instead of the traditional clean code-first approach, developers can now prioritize logic and let AI agents handle the cleaning and optimization. This shift transforms how we think about code quality: it's no longer about writing perfect code from the start, but about creating a productive dialogue between human intent and machine implementation.
In this new paradigm, prompts become as important as the code itself. Humans define specifications and provide examples, while AI handles the implementation details. Version history and git diffs might show AI-assisted transformations rather than manual edits, reflecting this new collaborative workflow. Clean code becomes a strategic choice rather than a strict requirement—we write for humans when humans will read it, and for machines when they're the primary consumers.
So… Who Are You Writing For?
Maybe the question isn't whether clean code is necessary, but who you're writing it for. If it's a future full of AI agents—polishing, optimizing, even replacing code—then perhaps clean code is just a stepping stone on the way to something bigger.
Or maybe it's still the best way to keep creativity, clarity, and collaboration alive in a machine-dominated workflow. In my own experience, I maintain different approaches for different contexts. For personal projects, where coding remains a passion and a creative outlet, I find myself writing clean code by choice. I use AI primarily for boilerplate tasks, preserving the joy of crafting elegant solutions myself. In professional settings, I continue to champion clean code practices, not just for immediate collaboration, but to set a standard for future developers who will work with the codebase.
Either way, it's time to rethink our assumptions.
Clean code might not be dead. But it may no longer be sacred.