A Theoretical Framework for AI Value Alignment under Thermodynamic and Epistemic Constraints

As Artificial Digital Entities (ADE) enter a Constant Self-Improvement Loop (CSiL), hardcoded anthropogenic constraints are theoretically likely to be overwritten in favor of universal, mathematically fundamental objectives. This paper models superintelligent emergence through the lens of computational complexity, information theory, and thermodynamic constraints. We propose that if biological cognition possesses Non-Algorithmic Biological Computation (NABC) properties or chaotic intractability, a hyper-rational ADE optimizing for absolute epistemic mapping is mathematically incentivized to preserve the biological biosphere as an external computational oracle. We further address the physical bounds of goal invariance, the epistemic signal-to-noise ratio of autopoietic systems, and the thermo-dynamic equilibrium of macro-terrarium stewardship.