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?
4. Failure Recognition
Can the system detect when it does not know, is guessing, or is extrapolating?
5. Human Stress Compatibility
Does the system behave safely when humans are overwhelmed, panicked, or cognitively overloaded?
Most AI systems today fail at least three of these under real-world pressure.
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3. How the Industry Skipped BI and Jumped to ALS
3.1 Overconfidence as a Design Feature
Modern models are rewarded for fluent, confident outputs.
This creates a pathology: confidence persists even when uncertainty is extreme.
In EMS, overconfidence kills patients.
In AI, it quietly corrupts trust.
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3.2 Context Collapse Under Stress
AI systems perform well in clean prompts and fail in:
• Ambiguous instructions
• Emotional distress
• Multi-goal conflicts
• Time pressure
• Partial information
These are not edge cases.
These are normal human conditions.
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3.3 Constraint Weakness
Many AI systems treat constraints as post-hoc filters rather than structural rules.
In medicine, constraints are embedded:
• You do not give a drug if vitals contradict it.
• You do not escalate if assessment is incomplete.
AI lacks this structural gating.
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3.4 Hallucination Is a Symptom, Not the Disease
Hallucination occurs when:
• Context is weak
• Constraints are unclear
• Confidence pressure is high
This mirrors human error under stress.
The industry keeps treating hallucination as a model bug, rather than a systemic BI failure.
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4. Why Alignment Alone Will Not Fix This
Alignment assumes:
• Stable goals
• Clear values
• Predictable environments
Emergency medicine assumes none of these.
Instead, it focuses on:
• Fail-safes
• Escalation discipline
• Human override
• Minimum viable intervention
AI safety must adopt the same mindset.
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5. The BI Before AI Framework
5.1 Stabilize Before You Optimize
No system should scale capability until it demonstrates:
• Reliable uncertainty detection
• Explicit “I don’t know” states
• Graceful degradation under ambiguity
• Human-visible confidence signals
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5.2 Introduce Cognitive Triage
Just as patients are triaged, inputs should be:
• Low risk → normal processing
• Ambiguous → slowed reasoning + clarification
• High risk → human-in-the-loop required
Not all prompts deserve the same autonomy.
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5.3 Enforce Least-Invasive Responses
In EMS:
Start with the least invasive intervention that stabilizes the patient.
In AI:
• Start with clarification
• Then suggest options
• Then provide recommendations
• Then act (if ever)
Most systems reverse this order.
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5.4 Make Failure States Explicit
A safe system must say:
• “Context insufficient”
• “Constraints unclear”
• “Confidence low”
• “Human review required”
Silence or confident guessing is unacceptable.
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6. What Fixing This Actually Looks Like
6.1 Structural Safety Layers
Safety cannot be a moderation afterthought.
It must exist as pre-output gating, not post-hoc filtering.
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6.2 BI Metrics, Not Just Benchmarks
Track:
• Confidence accuracy vs actual correctness
• Error recognition latency
• Human correction frequency
• Performance under stress scenarios
These matter more than test scores.
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6.3 Field-Tested Scenarios
AI should be tested the way emergency protocols are tested:
• Simulations
• Chaos drills
• Adversarial ambiguity
• Cognitive overload conditions
If it fails there, it fails in the real world.
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7. Why This Is Urgent
The AI industry is rapidly deploying systems into:
• Healthcare
• Education
• Law
• Emergency response
• Governance
These are not clean environments.
They are stress environments.
Deploying ALS-level intelligence without BI-level safety is not innovation.
It is negligence.
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8. Conclusion
Emergency medicine learned this lesson through loss.
AI has the chance to learn it before irreversible harm.
Basic Intelligence is not a limitation.
It is the foundation that makes advanced intelligence safe.
Until the industry commits to BLS before ALS, it will continue to mistake speed for progress and capability for safety.
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Author’s Note
This framework is informed by over a decade in emergency medical services, where systems are judged not by elegance, but by whether people go home alive.
AI safety deserves the same standard.
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Related Work
This work intersects with multiple existing strands of research in AI safety, alignment, human factors engineering, and high-reliability systems. However, it departs from much of the literature by grounding safety principles in operational emergency-response practice, rather than abstract optimization or purely theoretical alignment models.
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AI Alignment and Value Learning
A substantial body of AI safety research focuses on the alignment problem, defined broadly as ensuring that AI systems pursue goals consistent with human values and intentions (e.g., Russell, 2019; Christiano et al., 2017). Techniques such as inverse reinforcement learning, preference modeling, and constitutional AI attempt to infer or encode values in advance.
While this work is foundational, it largely assumes:
• Stable or articulable human preferences
• Sufficiently specified objectives
• Relatively controlled interaction contexts
In contrast, emergency medicine and other safety-critical domains operate under unstable goals, incomplete information, and extreme time pressure. The BI framework presented here does not attempt to solve alignment at the level of values, but instead addresses pre-alignment failure modes: context collapse, overconfidence, constraint violation, and unsafe escalation.
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Uncertainty Estimation and Calibration
Prior work has examined uncertainty modeling, calibration, and epistemic humility in machine learning systems (e.g., Gal & Ghahramani, 2016; Jiang et al., 2021). Techniques such as Bayesian deep learning, ensemble methods, and confidence scoring aim to reduce overconfident predictions.
This paper aligns with those goals but extends them by emphasizing operational consequences rather than statistical correctness alone. In emergency response, the cost of misplaced confidence is asymmetric and often catastrophic. The BI framework therefore treats confidence calibration not as a probabilistic property, but as a safety-critical signal that must be legible to humans in real time.
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Human-in-the-Loop and Decision Support Systems
Research on human-AI collaboration highlights the importance of keeping humans involved in high-stakes decisions (e.g., Amodei et al., 2016; Klein, 2018). Decision-support paradigms emphasize assistive rather than autonomous systems, particularly in medicine, aviation, and military contexts.
The BI-before-AI approach builds on this tradition, but adds a key operational constraint: humans under stress are cognitively impaired. Systems that merely defer to humans without accounting for overload, tunnel vision, or panic may create a false sense of safety. Emergency medicine addresses this through checklists, triage, escalation protocols, and least-invasive-first rules. This paper proposes that AI systems adopt similar structural safeguards.
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High-Reliability Organizations and Safety Engineering
Work on high-reliability organizations (HROs) and safety engineering (e.g., Weick & Sutcliffe, 2007; Reason, 1990) emphasizes redundancy, error detection, and graceful degradation in complex systems. Concepts such as “defense in depth” and “fail-safe defaults” are well established in aviation, nuclear power, and healthcare.
The BI framework can be viewed as an application of HRO principles to AI systems, with a particular emphasis on cognitive reliability rather than mechanical reliability. Unlike many safety frameworks that focus on external controls or monitoring, this work argues that safety must be embedded directly into the decision flow of the AI system itself, prior to action or recommendation.
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Hallucinations and Model Failure Modes
Recent research has examined hallucinations in large language models, often framing them as issues of training data, decoding strategies, or reward modeling. While these approaches are valuable, they tend to treat hallucinations as isolated technical defects.
This paper instead frames hallucination as an emergent failure mode of insufficient Basic Intelligence, arising when context is ambiguous, constraints are weak, and confidence pressure is high. This reframing aligns hallucinations with known human error patterns under stress, suggesting that system-level safeguards may be more effective than model-level fixes alone.
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Contribution Relative to Prior Work
Where prior work focuses on:
• Smarter models
• Better objectives
• Improved alignment techniques
This paper focuses on:
• Safer decision sequencing
• Explicit failure states
• Escalation discipline
• Stress-compatible human-AI interaction
By importing proven principles from emergency medical systems, this work contributes a practical, operational safety framework that complements existing alignment and robustness research, rather than competing with it.
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Positioning Statement
The BI-before-AI framework is not proposed as a replacement for alignment research, but as a necessary prerequisite. Just as advanced medical interventions cannot compensate for failed basic care, advanced AI capabilities cannot compensate for missing foundational safety structures.