Mostly here to learn. I’m trying to untangle some intuitions about intelligence, ethics, and agency — especially where LLMs and alignment are concerned. I don’t claim to have answers, but I’m drawn to the kinds of questions that don’t let you go.
Background is in infrastructure and system-level thinking (data centers, large-scale build environments). Currently exploring how machine learning systems might interpret context, evolve values, or get it wrong in strange and quiet ways.
If I post, it’s usually to test an idea in public — not to defend it. I appreciate thoughtful pushback and clean counterarguments more than likes.
This metaphor really stuck with me: “It’s not about how smart AI is. It’s about how safe we feel with AI when it is wrong.”
I’ve been circling something similar in my own work—what if “alignment” is less about perfect obedience and more like the relational scaffolding we use in human dynamics? Not control, but trust, repair, transparency.
There’s a tension I keep bumping into: we want AI to surprise us (creativity, insight), but we also want it to never surprise us in dangerous ways. Maybe part of the answer isn’t in technical capability but in how we shape the culture around AI: who gets to audit it, who it listens to, and how it owns its mistakes.
Curious if you’ve explored frameworks or design principles that treat AI development more like relationship architecture than engineering?