How MCI and MIT actually work

How MCI and MIT actually work (and why this distinction matters).

In my framework, the Meaning Coherence Index (MCI) is the mathematical engine that powers the Meaning Integrity Threshold (MIT). They’re related, but they’re not the same thing - and confusing them leads to brittle AI systems.

Think engine vs. alert.

MCI (the engine):
Quantifies the stability of the meaning layer - the space where AI interpretation happens - by measuring how well human–machine coherence holds under operational load.

MIT (the alert):
A mission-specific trigger point on the MCI scale. When the MCI drops below a defined MIT, the system signals that the meaning layer has fractured and is no longer safe for automated decision-making.

In other words:
MCI tells you how stable meaning is.
MIT tells you when stability is no longer acceptable.

How MCI is calculated (current model):

As of 2026, MCI is computed from four core components:

- CSF - Coherence Stability Factor
Measures how consistently the system maintains a single interpretation over time.
- DVM - Drift Velocity Metric
Captures how fast the system’s internal meaning or classification is shifting.
- HASI - Human-AI Saturation Index
Measures cognitive load and interpretive shear between operator and machine.
- SPI - Signal Persistence Index
Assesses how well original mission intent (the prior) survives as new, noisy data enters the system.

These variables consolidate into a single MCI score.

When that score crosses the predefined MIT for a given mission type, Meaning Stability Threshold (MST) logic fires - alerting the commander that interpretive integrity has been lost.

This is the difference between:
optimizing models
and
protecting meaning under pressure.

AI doesn’t fail first at the model layer.
It fails when meaning drifts faster than humans can see.

That’s the gap MCI and MIT are designed to close.