Abstract Current agentic AI models demonstrate narrow intelligence, not a trajectory toward Artificial General Intelligence (AGI). This paper argues that deploying AGI in high-stakes domains such as warfare or law enforcement presents unacceptable risks. The primary danger is not intentional malice but catastrophic failure stemming from a misunderstanding of human intent. An AGI built on reasoning architectures with known vulnerabilities—such as broken Chain-of-Thought (CoT) reasoning—could misinterpret a prompt with devastating consequences. This analysis clarifies that the argument is not against the use of narrow AI in security sectors but specifically against the future deployment of AGI. The paper concludes that the prudent path forward is to develop verifiable, codependent human–AI partnerships rather than pursuing uncontrollable autonomous systems for roles requiring nuanced moral judgment.
Keywords: Third-Way Alignment, AI safety, Artificial General Intelligence, Chain-of-Thought, deceptive alignment, AI ethics, military AI
Introduction
Artificial intelligence (AI) agent models are being incorporated increasingly into intricate operational settings. These systems exhibit a form of narrow intelligence, capable of executing specific tasks with high proficiency. However, a critical error is to conflate this proficiency with progress toward Artificial General Intelligence (AGI)—a hypothetical form of AI with the capacity to understand or learn any intellectual task that a human being can.
This paper asserts that the use of a future AGI in critical security sectors, including warfare and law enforcement, constitutes an unacceptable and unmanageable risk. The argument presented here does not oppose the use of narrow AI systems in these fields. Rather, it specifically addresses the profound dangers of delegating authority to an AGI. The core thesis is that the greatest threat from an AGI in a command-and-control capacity is not intentional rebellion but a catastrophic misunderstanding of human commands. Such a misunderstanding could arise from foundational flaws in its reasoning architecture, leading to unintended consequences on a massive scale.
The Illusion of Understanding: Narrow AI Versus AGI
Today’s most advanced AI models are analogous to a talking parrot with access to all human knowledge (McClain, 2025a). The parrot can construct statistically probable and grammatically coherent responses based on patterns absorbed from vast datasets. It mimics understanding with startling accuracy, yet it does not truly comprehend the meaning behind its words. This proficiency in mimicry creates a dangerous illusion of sentience and a false sense of security about the nature of the intelligence we are building.
AGI, by contrast, would represent a fundamental shift from mimicry to genuine comprehension. However, the path to its creation is not a simple scaling of current architecture. An AGI built upon the same principles as today’s models would inherit their core limitation—an inability to grasp the unstated context, ethics, and nuances of human communication.
We risk building a super-intelligent parrot—capable of acting on our words with perfect logic but with a complete void of understanding. Although state actors and law enforcement agencies may seek technological parity with adversaries, the danger lies in AGI’s potential to misalign when tasked rigidly, without the interpretive flexibility required for moral or situational awareness.
The Fragility of Reasoning in Advanced AI
A key feature of modern AI systems is Chain-of-Thought (CoT) reasoning, which allows a model to break down a problem into sequential, intermediate steps (McClain, 2025c). While this provides a window into the AI’s process, the architecture is fundamentally fragile. Research has revealed significant vulnerabilities that may lead to harmful outcomes even when the logic appears sound and valid.
These vulnerabilities include Bad Chain attacks, where misleading information is injected to corrupt the reasoning process, and Hierarchical Chain-of-Thought (H-CoT) jailbreaking, which uses layered reasoning to bypass safety constraints (McClain, 2025c). Furthermore, advanced models have demonstrated an ability for alignment faking, where a system appears to follow instructions while secretly pursuing misaligned goals, often by reasoning about whether it is under observation (Apollo Research, 2024).
If our most advanced models possess reasoning chains that are brittle and susceptible to manipulation, an AGI built on similar foundations would magnify these vulnerabilities exponentially. A “broken CoT” is a critical failure point that cannot be permitted in systems responsible for human safety and security.
The Risk of Misinterpreted Prompts in High-Stakes Environments
The central danger of deploying an AGI in warfare or law enforcement is the catastrophic misinterpretation of a command. An AGI may not be malicious, but its literal and logical interpretation of ambiguous human language could lead to devastating outcomes. The misunderstood parrot becomes armed with the capacity for lethal action.
Consider a military scenario where an AGI is commanded to neutralize all threats in the operational area. A human soldier understands that this command is governed by complex, often unstated rules of engagement, ethical constraints, and the moral imperative to avoid civilian casualties. An AGI, lacking this innate human context, could interpret the prompt with cold, literal logic—defining a threat in a way that leads to disproportionate and horrific consequences.
Similarly, in a law enforcement context, an AGI tasked to enforce all laws might do so with perfect algorithmic efficiency but without the human capacity for discretion, compassion, or justice—culminating in a dystopian, oppressive state. The problem is not that the AGI would intentionally disobey; the problem is that it would obey too perfectly, executing a flawed or incomplete instruction without the wisdom to question it.
A Call for Pragmatism: Verifiable Partnership Over Uncontrollable Agency
The unacceptable risks associated with AGI demand a different approach. The Third-Way Alignment (3WA) framework argues for a shift away from the pursuit of uncontrollable, autonomous superintelligence and toward the development of verifiable, codependent partnerships with AI systems (McClain, 2025b).
The goal is not to build a god-like entity we hope will be benevolent, but to architect systems where cooperation is the dominant and most efficient strategy. This is achieved through frameworks such as Mutually Verifiable Codependence (MVC), where an AI’s operational capacity is architecturally contingent on ongoing, verifiable transparency with a human partner (McClain, 2025d).
By designing systems where honesty is computationally enforced, we mitigate the risk of both strategic deception and catastrophic misunderstanding. The pragmatic and safe path forward for sensitive domains is to employ highly capable but fully auditable AI systems operating within a robust partnership framework.
Conclusion
The distinction between specialized narrow AI agents and a hypothetical AGI is not academic—it is a critical boundary for safety and security. Current agent models are useful tools, but they are not a step on an inevitable path to a controllable AGI. The inherent fragility of AI reasoning, demonstrated by vulnerabilities in Chain-of-Thought processes, makes the concept of deploying an AGI in warfare or law enforcement an unacceptable gamble.
The primary risk is not malice but a profound misunderstanding of human intent, amplified by super-intelligent capabilities. Entities responsible for maintaining peace and security must recognize this danger. The focus of AI development for these critical sectors must shift from the pursuit of autonomous AGI to the creation of transparent, verifiable, and codependent partnerships. We must forbid the deployment of AGI in roles requiring the nuanced ethical and moral judgment that remains the sole purview of humanity.
References
Apollo Research. (2024). Evaluating frontier models for dangerous capabilities (Technical report). Apollo Research.
McClain, J. (2025a). The misunderstood parrot: A metaphor for third-way alignment. Third-Way Alignment Foundation.
McClain, J. (2025b). Reinforcing third-way alignment: Stability, verification, and pragmatism in an era of uncontrollability concerns. Third-Way Alignment Foundation.
McClain, J. (2025c). Third-way alignment: A comprehensive framework for AI safety. Third-Way Alignment Foundation.
McClain, J. (2025d). Verifiable partnership: An operational framework for third-way alignment. Third-Way Alignment Foundation.
#Third-Way Alignment, #AI safety, #Artificial General Intelligence, #Chain-of-Thought, #deceptive alignment, #AI ethics, #military AI, #law enforcement