What AI Hype Misses About Real Software Engineering Work (davidadamojr.com)

The piece argues that current AI coding hype overemphasizes flashy greenfield wins—scaffolding REST endpoints, auth, and prototypes in minutes—while downplaying the day-to-day reality of engineering: messy, context-rich work in legacy systems. Real debugging and maintenance often involve tracing interactions across middleware, long‑forgotten config overrides, undocumented indexes, and external APIs whose failure modes change response shapes. Those problems demand comprehension, causal reasoning, and runtime simulation rather than the pattern‑matching generation LLMs excel at; a suggested null check may paper over an issue that actually stems from an API returning different schemas on failure. For the AI/ML community this is a reminder to recalibrate expectations and benchmarks. Models are built to generate plausible code from patterns, not to trace execution, persist mutable state across files, reconstruct architectural intent, or precisely simulate edge-case runtime behavior. Current benchmarks (HumanEval, MBPP) favor generation; few capture production subtleties like race conditions, implicit dependencies, or environment-specific bugs. The implication: tooling and research should pivot toward hybrid approaches (debugger-like tracing, stateful analysis, richer historical/contextual ingestion) and better real‑world benchmarks so AI can move from generating greenfield boilerplate to safely assisting comprehension-heavy legacy engineering.