You're right that current systems aren't close to that level of reasoning.
What I'm wondering is whether we can approximate some of it structurally — by defining when execution is allowed or not — even without that level of sophistication in the model itself.
Curious how far you think simple constraint systems can go before something like that kind of reasoning becomes necessary.
I think you're reading it more strongly than I intended.
The point about "ownership" in that document is more about where authority over execution sits, not about restricting what AI is allowed to reason about.
So it's not saying "AI shouldn't reason about things it doesn't own," but rather asking who has the authority to define and enforce the conditions under which actions are allowed to execute.
I agree that in current systems (like smartphones), a lot of this is already handled through predefined constraints.
What I'm trying to explore is whether that idea needs to be extended or structured differently when the system has more autonomy and operates in less predictable environments.
That's really interesting — thanks for sharing the notes.
The "rebel agent" framing feels very close to what I'm trying to get at, especially the idea that refusal can be part of correct behavior rather than failure.
One difference I'm trying to think through is where that decision lives.
In a lot of these examples, the agent itself decides to deviate based on its understanding of the situation.
What I'm wondering is whether we can (or should) define that earlier — at the level of the action itself.
So instead of the agent deciding to "rebel" at runtime, the system would already encode when execution is permitted, and refusal becomes the default if conditions aren't met.
The explanation part you mentioned also seems important — not just saying "no", but making it legible why execution wasn't allowed.
Curious how much of that work treats rebellion as something emergent from the agent, vs something structurally defined in the system.
I think you're right that at the model level, competition pushes toward "always say yes."
What I'm wondering about is whether control needs to exist at a different layer — not in the model itself, but in the system that decides whether actions are allowed to execute.
In other words, even if a model is willing to say "yes," the system using it might still need to decide whether execution is permitted.
Otherwise, it feels like we're relying entirely on model behavior for safety, which seems fragile in competitive environments.
Example: Caution: heat risk