Beyond the Hype: Why Your AI Assistant Might Be Sabotaging Your Architecture (medium.com)

AI coding assistants like GitHub Copilot, ChatGPT and Claude can seduce developers into over-engineering by proposing elegant-sounding abstractions that aren’t aligned with real usage patterns. The piece recounts a concrete Rust case (AIRS‑MCP) where a working STDIO transport was replaced by layers of AI-suggested traits and generics—TransportBuilder, generic MessageHandler<T>, complex context types—leading to weeks of extra work, 265+ commits, unreadable type errors, and a broken correlation mechanism that dropped responses. The danger is the “trust trap”: AI presents confident, plausible designs that trigger developer deference, so theoretical benefits (extensibility, testability) are optimized at the expense of cognitive overhead, debuggability, and functionality. Technically, the article highlights recurring anti‑patterns: sophistication bias (unnecessary advanced language features), abstraction cascades (builders and traits nobody actually uses), and over-genericization (T often = () in practice, causing ubiquitous type annotations and confusing errors). The implications for the AI/ML and engineering community are clear: treat AI suggestions as hypotheses, not authority—favor YAGNI, incremental refactors, realistic usage analysis, rigorous tests and reviews, and measure actual benefits before introducing cross-cutting abstractions. Otherwise, AI-driven “improvements” can quietly sabotage architecture and developer productivity.