The author, a Rust developer, argues that language features remain critical even when using AI coding agents. While agents can generate code, human review is necessary to ensure the quality and logic of the implementation, as agents may prioritize successful compilation over optimal design.
In a comparison of AI agent performance, using Opus 4.6 to build a simplified Git implementation resulted in 14% more output tokens for Rust compared to Go, while GPT-5.4 showed an 11% difference. These variations in token usage can impact costs, with prices for one million output tokens ranging from $4.40 for GLM-5.3 to $25 for Opus 5.
Rust provides features like immutable variables by default, explicit mutability, and robust error handling via Result and the question mark operator. These tools help avoid common pitfalls, though the borrow checker can be challenging for both human developers and AI agents, sometimes leading agents to use excessive cloning or unwrap calls to bypass compilation errors.
The author notes that while Rust compilation times can be slow, recent versions have shown improvements, such as the hyperqueue project build time dropping from 26.1 seconds in version 1.61 to 14.7 seconds in version 1.87. Additionally, the ecosystem benefits from Cargo, which integrates dependency management, testing, and building into a single tool.
To maintain code quality, the author suggests using tools like Clippy with strict linting rules, such as unwrap_used and expect_used, to prevent AI agents from bypassing safety checks. Ultimately, the author emphasizes that while AI can assist in navigating Rust's learning curve, human oversight is essential to prevent logic errors and ensure the code meets project requirements.
