AI's Legitimacy Crisis: Moving Beyond Prediction to Verifiable Execution

The article argues that the primary problem with AI is not hallucination, but a lack of 'execution legitimacy'. Modern AI systems focus on predicting the most likely next state, which is insufficient for real-world applications where the only allowed action matters. The author proposes a paradigm shift from predictive systems to 'compilation systems', where every output corresponds to a unique, deterministic, and physically verifiable execution path. This approach, termed 'Digital Materialization', rests on three axioms: no-hallucination, decoupled generation, and power efficiency. The author draws parallels to existing engineering practices, such as world models, systems theory, and control theory, which provide a foundation for building reliable systems. The article concludes that systems entering the physical world must be predictable, provable, and never improvise under uncertainty. While this approach may involve higher computational costs and harder modeling, it offers trustworthiness that generative AI cannot guarantee.

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