AI Writes the Code. You Better Know If It's Wrong (davidadamojr.com)

An engineer chasing a production AB-BA database deadlock used an AI coding assistant to locate the contention between a web thread and a background job. The AI suggested a pessimistic row lock (e.g., record.lock() / with_for_update), but initially misled the author about lock lifecycle and exception behavior. After probing, the AI corrected the code to wrap locking in a transactional pattern (with_lock) and proper error handling. The key technical takeaway: AI can write the “how” (syntax and patterns) but may miss crucial operational details—locks must be acquired/released correctly, exceptions can interrupt flow, and holding exclusive resources during unreliable I/O (like S3 uploads) can exhaust DB connections and cascade failures. More broadly, this illustrates a shift in engineering skill requirements: pattern recognition (race conditions vs exceptions vs resource leaks), trade-off analysis (pessimistic vs optimistic locking, unique constraints, distributed locks), and mental simulation of failure modes under load. AI accelerates code generation, but humans must supply context—concurrency expectations, retry cost, transaction complexity, and tolerance for added infrastructure—so they can choose the right strategy and anticipate impacts (throughput loss, retries, availability tradeoffs). The implication for the AI/ML community: as AI takes over boilerplate coding, emphasis should move to systems thinking, causal reasoning, and judgment to validate and harden AI-produced code.