Neural Networks Validate Thermodynamic Theory of Complex Systems

I've been developing a framework extending Prigogine's dissipative structures theory to explain how systems organize around energy gradients. The unexpected validation came from AI research: neural networks spontaneously exhibit the same organizational principles I observed in biological and economic systems.

Key findings: The Lottery Ticket Hypothesis demonstrates what I call Dissipative Capital Formation—systems investing resources to build structures enhancing future capacity. Emergent abilities in LLMs at critical scales match predicted phase transitions. Information Bottleneck Theory aligns with how biological systems manage gradients.

The framework (Gradient-Coupled Systems Theory) proposes that systems sharing energy/information gradients become thermodynamically coupled, creating the hierarchical organization observed everywhere from cells to markets. I've outlined mathematical pathways using information theory, optimal control, and statistical physics.

Looking for collaborators to formalize the mathematics and validate predictions in specific domains. Early stage, no institutional backing. Welcome skeptical analysis.

Documentation: https://creordics.org