Abstract

In emergency medicine, no matter how advanced technology becomes, outcomes depend on whether Basic Life Support (BLS) is performed correctly before Advanced Life Support (ALS) is attempted. Skipping basics does not accelerate care; it increases mortality.

The modern AI industry is making the same mistake repeatedly. It is racing toward increasingly capable systems while failing to stabilize foundational elements of human-AI interaction: context clarity, constraint enforcement, confidence calibration, failure recognition, and human cognitive limits under stress.

This paper argues that the majority of AI safety failures are not problems of alignment, autonomy, or intelligence, but of missing Basic Intelligence (BI). Drawing from real-world emergency response principles, this paper introduces a BI-first framework that treats AI safety as an operational discipline rather than a theoretical one, and proposes concrete mechanisms to stabilize systems before scaling capability.

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1. The EMS Analogy the AI Industry Keeps Ignoring

In emergency medicine:
• BLS includes airway, breathing, circulation, hemorrhage control, and patient assessment.
• ALS includes intubation, cardiac drugs, invasive procedures, and advanced diagnostics.

Every paramedic is trained on one rule:

ALS never compensates for failed BLS.

If you intubate a patient whose airway positioning was never corrected, you fail.
If you push drugs without understanding the underlying rhythm, you fail.
If you escalate complexity before stabilizing basics, patients die.

The AI industry is currently attempting ALS-level intelligence on systems that have not passed BI-level stability checks.

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2. What “Basic Intelligence” Actually Means

Basic Intelligence (BI) is not about model size, benchmarks, or reasoning depth.
It is about cognitive safety and operational reliability.

BI consists of five core capabilities:
    1.    Context Integrity
Does the system accurately understand the situation it is responding to?
    2.    Constraint Awareness
Does the system know what it is not allowed to do?
    3.    Confidence Calibration
Does the system adjust certainty based on ambiguity, data quality, and risk?
    ...

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