Predictions

There exist states where delivering intervention produces worse outcomes than withholding, because the intervention itself functions as an uncontrollable demand on a system that has lost capacity to process demands.

Consumer sensors (HRV, EDA, pupillometry) combined with AI-derived conversational/behavioral signals can resolve the state distinctions the architecture requires -- at minimum, can-process vs. cannot-process.

State-conditioned bounded actions (state legibility, choice compression, micro-controllability tasks) reduce time-to-regulation without creating dependency or reducing long-term autonomous capacity.

LLM-based semantic modeling of the human's values and context improves system performance beyond what sensor-plus-rules achieves alone.

Capacity improvements are maintained after the system is removed; the system builds the human's own regulatory capacity rather than substituting for it.