Physical AI Safety: Ownership and Execution Boundaries

This document offers a genuinely innovative perspective on AI safety — one that reframes the problem in a way that most alignment discussions have missed. I’d like to offer a few thoughts.

1. Fixed Label Is Not a Benefit — It’s an Obligation

In the comments, the author frames Fixed Label as something beneficial to manufacturers — as evidence of fulfilled responsibility. But this framing is wrong. The moment you describe it as a benefit, it becomes optional.

The logic is already inside this document:

• A manufacturer built a product that enables AI control

• If an accident occurs, the manufacturer bears responsibility

• Without Fixed Label, there is no declared basis for AI judgment

→ Providing Fixed Label is an obligation, not a choice.

The document should have stated clearly: “The moment you allow AI to take control, this is required.” Not “this would be good for you.”

The specific JSON form proposed for Fixed Label is one valid implementation — but the form itself can and should be flexible. What cannot be flexible is the obligation to declare.

2. From the Age of Human Control to the Age of AI Control

There is a fundamental paradigm shift that this document implicitly addresses but does not name directly.

When humans controlled devices: the unit of thinking was the device. Remote control was the core innovation — overcoming physical distance. The assumption was: “Do this while I’m away.”

When AI controls actions: the unit of thinking must be the action. The core value is no longer overcoming distance — it is replacing repetitive tasks. The assumption becomes: “Do this so I don’t have to keep doing it,” even while the person is present.

This is why the shift from device-centric to action-centric thinking matters. In the era of remote control, there were only a handful of devices to enumerate. Hardcoding safety rules was possible because the target set was finite.

As robots become capable of interacting with every physical object in an environment — not just smart devices with digital interfaces, but any object that can be touched, moved, or manipulated — that finite set becomes infinite overnight.

This is precisely why Fixed Label becomes necessary. The manufacturer or agent developer who defines an action knows its physical limits. That knowledge must be declared — for the same reason a user manual must be provided.

3. The Limits of Hardcoding and Alignment

Once robots engage with the full physical world, both dominant approaches to AI safety break down:

Hardcoding / whitelists: Defining device types and encoding safety rules per platform becomes impossible the moment the target set expands without bound.

AI Alignment: The premise that “more training data and better fine-tuning will produce correct judgment” collapses in an infinite, real-time physical context. And even if AI makes a correct judgment — whether AI should be making that judgment at all is a separate question entirely.

The liability shift follows directly:

• When a human ignores a user manual and causes an accident → user’s fault

• When a manufacturer allows AI control but provides no Fixed Label → manufacturer’s fault

The moment AI is permitted to take control, the obligation to provide information transfers from the user to the AI. Failing to fulfill that obligation is a product liability failure — not a missed opportunity.

A Personal Reframing of This Document

“The Good AI” Illusion vs. Structural Safety

Critics have long attacked opaque black-box AI systems. But the alternative they proposed — AI Alignment — is itself an extension of the same assumption: “If we train AI well enough, it will behave correctly.” This is intelligence-as-solution thinking.

• Old approach (training as virtue): “If AI becomes smart enough (99.9%), it will be careful on its own.” → When it fails, the cause cannot be traced.

• This document’s approach (bulletproof vest design): “How smart AI is doesn’t matter. What matters is whether there is a checklist that prevents it from crossing physical boundaries.” → When it fails, responsibility is traceable.

Abstract Commands vs. Atomic Actions

Previously, a command like “dance” was treated as a single unit, and alignment was expected to handle it wholesale. This document points out that inside that command are countless discrete physical events (Actions).

• Old approach: “Dance, but be careful not to hit anyone.” (relies on inference)

• This document’s approach: “Dancing is a set of direction changes and accelerations. Before each Action executes, verify it passes the safety constraints declared by the designer.” (relies on verification)

The Discovery of “Who to Ask”

The most decisive difference is the source of the answer.

• Old approach: AI searches its own training data and answers, “This seems safe.” (Humans trust this hallucination.)

• This document’s approach: “Ask the manufacturer for the physical limits of this action. Ask the user for the intended purpose of this action.”

Conclusion

The existing AI alignment paradigm was a collective human hallucination — the belief that an abstract command like “dance” could be made safe through AI intelligence alone.

This document proposes a different architecture: decompose the command into atomic actions , and seek the physical truth of each action from the designer who actually knows it.

In the end, the real answer is not teaching AI morality. It is giving AI a address book of physical boundaries — declared by those who are responsible for them.