Bit Energy, Model Weight, and the FRA Cycle: Why AI Information Has Mass
Bit Energy, Model Weight, and the FRA Cycle: Why AI Information Has Mass
This note links Landauer’s limit with the FRA (Ξ–Φ–ℱ) cycle and treats an AI model as a physical object with non-zero mass–energy. We introduce three predicates for any model: existence (E_AI), potential (P_AI), and active inference (A_AI), and show how each step of computation corresponds to transitions Φ → ℱ → δ with irreversible heat dissipation (ΔE ≥ kT ln2 per bit operation). Stored parameters carry “model weight” even when the system is idle; power only creates readiness, and queries trigger real energy loss. Erasing the model is described as structural relaxation ℱ → Φ → Ξ. The FRA framework is presented as an engineering abstraction, not a strict theorem, clarifying how information persists, transforms, and vanishes in physical hardware.
information physics, Electric energy, Renewable energy, Entropy, information as physical, Quantum physics, Model weight, Energy balance, energy of information, Conventional energy, digital entropy, bit energy, information dissipation energy, Energy saving, Weight of AI, thermodynamics of computation, Energy utilisation, Landauer principle, Landauer limit, Energy, energy–information relation, computational entropy, Physics, information thermodynamics, Energy industry, entropy energy, Energy process, Energy conversion, bit–heat, Thermodynamic engineering, Shannon entropy energy, entropy of bits, Mathematical physics, Physics/methods, computational energy cost, Thermodynamics, AI Energy, information energy, Energy Intake, Energy technology, Theoretical physics, information mass
information physics, Electric energy, Renewable energy, Entropy, information as physical, Quantum physics, Model weight, Energy balance, energy of information, Conventional energy, digital entropy, bit energy, information dissipation energy, Energy saving, Weight of AI, thermodynamics of computation, Energy utilisation, Landauer principle, Landauer limit, Energy, energy–information relation, computational entropy, Physics, information thermodynamics, Energy industry, entropy energy, Energy process, Energy conversion, bit–heat, Thermodynamic engineering, Shannon entropy energy, entropy of bits, Mathematical physics, Physics/methods, computational energy cost, Thermodynamics, AI Energy, information energy, Energy Intake, Energy technology, Theoretical physics, information mass
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