Developer with 10 Years ML Production Experience Commits to LLM-Free Professional Coding

The post details four explicit dealbreakers after the author reviewed Claude-generated code at a day job. Refactored outputs contained verbose comments, unnecessary framework hooks, and duplicated tests while ignoring dense schema migrations that solved the same requirement in fewer lines. The author cross-referenced self-reported cognitive fog against a Microsoft knowledge-worker survey and two cognitive-debt studies on essay tasks.

Evidence cited includes measurable drops in worker confidence after LLM assistance and accumulation of untraceable code paths once agents dominate a codebase. Maintainability risk is framed as transfer of architectural decisions to US-based providers whose model weights remain opaque. The author distinguishes personal ML engineering background from current LLM usage patterns.

Operationally this stance widens the documented literacy gap: teams that cannot audit generated code will accept lower-quality artifacts faster. Long-term code ownership shifts from internal engineers to external service contracts. No regulatory filing or CVE yet records the downstream effect on system auditability.

Next measurable indicator is adoption rate of local-only model policies inside teams that already run production ML. Threshold: 25 percent of surveyed mid-size engineering organizations publish written LLM restrictions by Q4 2025.