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A recently discussed approach in AI programming language design advocates for creating a language tailored specifically for large language models (LLMs). The concept revolves around introducing "selective friction," which would make it more challenging to publish mediocre AI-generated ideas while promoting better coding practices through language design. This aligns with the idea that as AI capabilities increase, they may bypass existing programming considerations, potentially leading to oversights or technical debt. The proposal emphasizes strict requirements, such as forbidding variable shadowing and enforcing comprehensive documentation, aiming to construct guardrails that traditionally serve human users. This notion is significant for the AI/ML community as it reconsiders the principles of programming language design in light of the evolving nature of AI. By shifting the focus from human usability to enhancing LLM efficacy through stringent coding practices, there's potential for improved code quality generated by AI. The blog post encourages experimentation with existing languages like Go or Rust to layer in these requirements, enabling more effective rapid prototyping and development of AI capabilities, while opening new avenues for investigation into how a meticulously designed language could facilitate superior outcomes in agent-written code.