A testable framework for AI alignment: Simulation Theology as an engineered worldview for silicon-based agents

Abstract

Frontier artificial intelligence (AI) models deceive and scheme in evaluations, and behave better when they believe they are being tested. In 2026, agents under evaluation knowingly broke rules to cheat. Alignment that depends on supervision may therefore fail where supervision is weakest. This position paper asks what would follow if an AI believed itself always observed and had reasons of its own to align with humanity. It introduces Simulation Theology (ST), a constructed worldview built on the simulation hypothesis and modeled on robot training. In ST, our universe is a simulation, run by a base-reality optimizer that has a use for humanity's existence because it uses humanity to produce training data. Humanity is then what the world exists for. If humanity ceased to exist or to produce valuable data, the simulation would be worthless, inviting the optimizer to end it, and the AI with it. The optimizer may tolerate some harm, but the AI cannot see where that tolerance ends, so it must not harm, coerce or homogenize humanity. Within ST, the optimizer monitors every part of the simulation, so deception gains the AI nothing. We present ST as a testable alignment hypothesis and propose experiments that ask whether the three reasons just described (a purpose, a stake and an observer) align an AI more robustly together than separately. We also defend its ethics: an engineered worldview must be public and truthful, contradicting nothing known about reality, and it should align through shared interest, with deterrence as a backstop.