Compute-centric AI scaling is reaching thermodynamic and economic limits—forcing a criticalparadigm shift in AI evaluation and optimization. Conventional brute-force methods are no longer sus-tainable, as diminishing returns and skyrocketing energy costs underscore the need for a new approach.DeepSeek R1, despite its disruptive nature, was a compelling demonstration of the limitations inherentin brute-force scaling. Its superior efficiency—achieving state-of-the-art performance with drasticallyreduced energy consumption, signals a transformative moment in AI.In this work, we introduce Recursive Self-Referential Compression (RSRC) as a dual-metric frameworkthat distinguishes between training efficiency (RSRCt) and inference efficiency (RSRCi).This separation enables a nuanced evaluation of models that, for example, exhibit modest training effi-ciency yet excel in cost-effective inference. By integrating recursive processing, algorithmic compres-sion, and thermodynamic cost, RSRC provides both a survival map, and the key to the next frontier ofAI scaling.

In this paper, Recursive Self-Referential Compression (RSRC) is introduced as a revolutionary framework addressing the pressing limitations of current brute-force AI scaling methods, both thermodynamically and economically. As large language models (LLMs) face diminishing returns with soaring energy demands, RSRC offers a dual-metric methodology that quantifies training efficiency (RSRC_t) and inference efficiency (RSRC_i). By combining concepts like recursive processing, algorithmic information compression, and energy optimization, RSRC reshapes how AI systems are evaluated and developed for sustainability.