Abstract—Contemporary computing architectures governed by the classical Von Neumann paradigm present critical thermal dissipation inefficiencies and infrastructure costs due to the systematic and irreversible destruction of information in cache memory during redundant compilation cycles. This paper presents the Thermodynamic Elastic Compiler (TEC), a hardware-agnostic execution engine that intercepts instruction flows in real-time to optimize energy efficiency. Utilizing the Minimum Description Length (MDL) as a computable estimator of Kolmogorov Complexity, combined with Shannon Entropy measurement, the system dynamically switches between a traditional brute-force profile and an optimized reversible mode with ultra-low register erasure. Empirical tests executed at a reference temperature of 293.15 K demonstrate that, under cyclic and highly predictable data flows (compression ratio ≤ 0.255), the TEC engine reduces bit erasure by 99.5%, achieving a symmetric collapse in elemental quantum thermal dissipation down to a scale of ≈ 1.40 × 10⁻¹⁷ Joules per cycle. These results open a new commercial frontier in electricity expense mitigation for distributed data centers (Cloud Computing) and enable the operational viability of Artificial Intelligence on ultra-low-power peripheral devices (Edge AI). Keywords—Elastic Compilation, Landauer Limit, Kolmogorov Complexity, Shannon Entropy, Reversible Computing, Edge AI.