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This seminar guided by Malcolm Ramsay - St. Claire paper and research on Quantum Neural Thermodynamics in Neural Predictive Coding & AI Probalistuc Compression. 

We investigated how overwhelming information loads demand unsustainable energy outputs, revealing the limits of current infrastructures and the growing tension between data expansion and energy scarcity. AI scaling exposes a fundamental constraint: intelligence is thermodynamically expensive. 

The brain resolves this via predictive coding - minimizing free energy through hierarchical inference - whereas large language models approximate this through probabilistic compression over token space. However, current architectures externalize entropy costs into infrastructure, producing ecological strain. 

This asymmetry reveals a critical inefficiency: biological cognition is entropy - adaptive, whereas LLMs are entropy - extractive. Friston’s free - energy principle suggests a pathway - intelligence must minimize surprise relative to energetic cost. Without such integration, scaling laws risk collapse under resource constraints. The future of AI depends on energy - aware architectures that couple inference with thermodynamic optimization.