Most discussions around AI safety focus on what models know or whether outputs are correct.
But since 2019, I’ve been working on something slightly different:
What actually matters is what knowledge becomes usable; but also how quickly it transfers capability.
A piece of information isn’t neutral once it can be acted on. Some knowledge scales fast, compresses into action easily, and propogates realizable outcomes (good or bad).
So I’ve been developing a framework called the Leverage-Aware Governance Kernel (LAGK). LAGK is an 8-phase system that regulates how information moves from:
idea to understanding to action to impact
It tries to answer questions like: What capability does this knowledge transfer? How easily can it be assigned a use-case or scaled? What happens when it propagates across many actors? Should it be shared differently depending on context?
Instead of “allow vs block,” it focuses on shaping the form of disclosure: Open Guided Shielded or Sealed
I’m curious how this lands with people here. Do you think future AI systems need something like a disclosure governance layer, not just alignment at the model level?
If anyone wants to explore or critique it, I’d value that: https://lightrest-lagk.manus.space
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