Most AI failures don’t come from wrong answers.
They come from unnecessary answers.
As AI systems scale, human attention does not.
The real constraint is no longer intelligence — it’s output justification.
I’m not proposing “better speaking AI”.
I’m proposing that speech itself must be conditional.
ΔX is a simple invariant:
If an AI-assisted interaction does not produce a measurable positive improvement,
the correct behavior is to halt or remain silent.
Silence is not a failure mode.
It’s a valid result.
This reframes AI from a decision-maker into a measurement and verification layer.
Humans keep:
- intent
- trade-offs
- accountability
AI is restricted to:
- measurement
- verification
- conformity checks
The framework is documented as a fully auditable corpus (27 PDFs),
with explicit stop conditions and responsibility boundaries,
published with a DOI:
https://doi.org/10.5281/zenodo.18100154
Open question:
How do you formally define a system that knows when not to answer?
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