This Kaggle Benchmarking Challenge entry evaluated AI models on hardware-aware code generation. The goal was to generate a Zero-Copy Rust FFI engine for Python, respecting Apple M-series silicon's 128-byte L1 cache line. Models tested included Gemini 1.5 Pro, DeepSeek Coder V2, OpenAI GPT-4o/o1-preview, and Llama 3.1 (405B). Findings highlighted a significant software bias in AI code generation. Most models defaulted to x86 64-byte alignments, causing false sharing on Apple Silicon, and introduced serialization overhead like Serde or JSON, defeating the zero-copy objective. Only reasoning models like o1 demonstrated the ability to analyze cross-language pointer boundaries and ABI layouts. The benchmark strictly verified the presence of #[repr(C, align(128))] and raw pointer casts, avoiding LLM-as-a-judge evaluations. This revealed a critical gap: LLMs prioritize software consensus over hardware efficiency. To enable AI to architect low-latency infrastructure, explicit hardware constraints must be enforced during evaluation. The provided evaluation script demonstrates how the benchmark operates for future testing.