Introducing the Cognitive Load Collapse Predictor (CLCP)

We treat cognitive overload like weather:
uncomfortable, but survivable.

But across every exercise and AI-enabled mission I’ve studied, the pattern is the same:

Operational failure doesn’t start when people feel stressed.
It starts when humans and machines lose the ability to hold a stable interpretation under pressure.

That’s collapse.

So I built a tool for that moment.

The Cognitive Load Collapse Predictor (CLCP) is a left-of-boom model for the cognitive battlespace. It tracks four pressure stacks:

• operational tempo
• ambiguity and uncertainty
• adversarial stressors
• bandwidth and task saturation

and maps them against a single variable that really matters:

Interpretive Integrity.

On the CLCP map, you see three curves over time:

– human cognitive load
– AI drift index
– the Meaning Stability Threshold (MST)

When human and machine trajectories both bend toward that threshold while pressure spikes, you’re no longer in “stress management.”

You’re in a pre-collapse window.

This is the gap between “everyone feels busy” and
“the frame just broke and nobody noticed yet.”

We’ve had tools to monitor fires, comms, cyber, logistics.

CLCP is a tool to monitor something more fragile:
whether anyone can still see the battlespace clearly enough to make a coherent decision.

If interpretation is now a center of gravity,
we need left-of-boom warning for when it’s about to fail.