Structural Shift in Liability Premiums and Insurance Pricing Dynamics Under Zero-Marginal-Cost Prediction
A Formal Analysis from Actuarial GLMs and Simon's Collision Probability to Social Quantum Field Theory (SQFT) Abstract With the proliferation of Artificial Intelligence (AI) and Large Language Models (LLMs), the marginal costs of prediction and conditional expectation calculations have been driven toward zero. However, the probabilistic output and black-box nature of generative AI have not eliminated total systemic risk; instead, they have rendered the residual, unmodelable resource—final judgment and liability assumption—extremely scarce. This paper begins by augmenting actuarial Generalized Linear Models (GLMs) with deep learning representation features and incorporating Simon's Problem collision probability from cryptography and quantum information theory. We demonstrate that classical random sampling faces an exponential trial-and-error bottleneck of $\mathcal{O}(2^{n/2})$ (the birthday attack wall) when searching for loss-liability collisions within ...
