The Core Problem of AI Is Not Hallucination — It Is the Lack of Execution Legitimacy Janus pater Introduction Most debates around AI today revolve around a false question: is the model smart enough, accurate enough? In engineering reality, the real question is never accuracy — it is whether the system is even allowed to act. 1. The Original Sin of the Predictive Paradigm: No Execution Legitimacy Modern generative AI fundamentally does one thing: predict the most likely next state in a probability space. Whether it predicts tokens, pixels, latent states, or so-called “world models”, as long as the output is probabilistic, it answers only one question: “What is most likely to happen?” In many real-world systems, however, engineering demands an entirely different question: “What is the only action that is allowed to be executed?” This is not an accuracy problem — it is a legitimacy problem. 2. Yann LeCun Is Right — but Only Halfway LeCun is absolutely right to criticize next-token prediction as the foundation of intelligence. World models (JEPA) are undeniably more advanced than raw pixel or text prediction. Yet even world models still output possible worlds, not permitted worlds. World models are excellent at three things: • Abstract state representation • Learning dynamics • Producing goals and constraints They do not possess — and should not possess — execution authority. Once a probabilistic model is allowed to issue actions directly, the result is not intelligence — it is the statistical mean of accidents. 3. The Real Boundary: From Prediction to Compilation If AI is to enter the physical world, it must undergo a paradigm shift: From predictive systems → to compilation systems This boundary is not philosophical — it is enforced by engineering reality. Before execution, a system must satisfy a hard requirement: Every output must correspond to a unique, deterministic, and physically verifiable execution path. This is what I call the paradigm of Digital Materialization. 4. Three Non-Negotiable Axioms of Digital Materialization This is not about making AI smarter — it is about making it executable. The paradigm rests on three non-negotiable axioms: 1. No-Hallucination Axiom Any output without a unique physical mapping is illegal 2. Decoupled Generation Axiom Generation must be fully constrained by protocols, states, and boundary conditions 3. Power Efficiency Axiom Using probabilistic search to solve deterministic problems is pure energy waste 5. This Architecture Already Exists in Engineering The architecture of reliable systems has long been established: • World Model: understanding and planning (cognitive layer) • Systems theory: decomposition and closed-loop design • Partial Differential Equations: the programming language of physics • Control theory / MPC: the actual compiler • Execution layer: obeys compiled commands only Within this system: • Feasible → Execute • Infeasible → Refuse to act Refusal is itself a valid output. 6. This Is Not Anti-AI — It Makes AI Accountable to Reality The cost of this path is clear: • Higher computational cost • Harder modeling • Slower generalization In return, it offers something generative AI can never guarantee: （Trustworthiness） The systems that will truly enter the world will not be chatty generalists, but machines whose behavior is predictable, whose failure modes are provable, and who never improvise under uncertainty. Conclusion If a system’s failure can injure people, destroy assets, or cause irreversible damage, then it has no right to guess using probability. For AI to evolve from a toy into a tool, it must graduate from prediction — and submit to compilation.