The core failure mode is ontological, not just statistical.

Epistemic Status: Conceptual argument, grounded in examples from autonomous systems; proposing a general, generative architectural principle. Developed collaboratively with AI assistance.

Summary:
Some AI systems act as if they occupy a “god’s eye view”. This "objective" perspective presumes a stance of total, context-free knowledge. I suspect this is not just overconfidence or bad engineering; I believe this to be a flawed ontology. Even with perfect sensors and infinite compute, a system cannot escape its own frame to fully verify its correctness. The result is a predictable and structural failure mode I call engineered hallucination.

Relevance: This post critiques a hidden ontological assumption in many AI architectures and proposes a bounded alternative with direct implications for epistemic reliability and AI alignment.

1. The Ontological Fault Line

Gödel’s incompleteness theorem shows that no formal system can prove its own consistency from within itself. That’s not just a math quirk. I suspect it generalizes to any cognitive or computational agent. If an AI system’s ontology presumes that it can hold a complete, self-consistent model of itself and its environment, that ontology is already broken. The “god’s eye view” isn’t just unrealistic; it encodes a contradiction: to know everything, you must stand outside yourself. But you cannot.

2. Why Traditional Systems Survive This

In narrow, well-bounded systems like databases, compilers, transaction processors, the ontology and the environment match. Inputs, states, and rules are all definable and explicitly defined, and the system’s “world” is small enough to verify internally. The flaw only becomes dangerous in open-world AI systems, where the scope is unbounded and the ontology’s hidden assumption of completeness guarantees epistemic blind spots.

3. Example: Autonomous Driving

Take an autonomous vehicle. Its planner assumes its own perception and actuation systems are intact. But cameras fog over, brake lines corrode, and firmware drifts. The car’s internal model contains “the road” and “its own capabilities.” But over time, both will inevitably diverge from reality. When that drift is invisible to the omniscient computational per...

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