Why Ruby on Rails might be the perfect language for AI code generation

Ruby on Rails might be the ideal programming language for AI code generation due to its expressive efficiency and readability. Large language models excel at generating small-scale code but struggle with larger codebases that exceed their context windows. This creates a fundamental constraint that favors languages requiring fewer tokens to express complex functionality.

The big picture: Language models face diminishing performance as context windows fill with code, making token efficiency a crucial factor in determining which programming languages work best with AI assistants.

• The effectiveness of AI-assisted programming directly correlates with how many tokens are needed to express a given function or feature.
• Even models advertising large context windows experience degraded performance as more content is added, limiting their practical usefulness with verbose programming languages.
• Code completion tools like GitHub Copilot and Cursor remain the gold standard because they leverage AI’s strength in small-scale changes rather than attempting to generate entire applications.

Why this matters: Programming languages designed for developer happiness and concise expression, like Ruby, may provide significant advantages when working with token-limited AI systems.

• A language requiring fewer tokens per feature allows developers to build more complex applications before hitting the AI’s context limitations.
• This creates a counterintuitive advantage for languages that prioritize human readability and concise syntax over raw performance.

Technical considerations: The ideal language for AI code generation balances token efficiency with maintainability and readability.

• Languages with extensive boilerplate code (like Golang’s error handling patterns) consume valuable context window space that could otherwise be used for additional features.
• Unlike humans who can skim repetitive code patterns, AI models must process every token, making verbose languages less efficient for machine-assisted programming.
• Using minified code isn’t the solution, as AI models still need expressive variable names to understand program logic effectively.

Reading between the lines: Ruby on Rails’ focus on developer experience and convention over configuration makes it unexpectedly well-suited for AI programming, despite its performance limitations.

• While typed languages provide important safety checks that compensate for LLMs’ inability to test their own code, Ruby’s expressiveness may outweigh this disadvantage in certain scenarios.
• The argument for using JavaScript and Python remains strong due to their outsized presence in training data, but Ruby’s efficiency might eventually overcome this training bias.

The irony: Ruby, designed to be the most “human” programming language with its natural language-like syntax and focus on programmer happiness, may ironically become the preferred language for AI-driven development.