A Seven-Dimensional Dissipative-Structure Model of the Carbon–Silicon Coupled System and Four Evolutionary End-States

The long-term evolutionary direction of the carbon-silicon coupled system (humanity and AI) is one of the most contentious issues today. Existing discussions are often trapped in a binary narrative of "AI as a permanent tool" versus "inevitable AI loss of control," lacking a unified quantitative framework. Based on dissipative structure theory, this paper abstracts the coupled system into seven observable state variables (Computational Scale C, System Redundancy R, Information Processing Efficiency ε, Recursive Self-Optimization Depth D, System Entropy S, Endogenous Information Ratio η, and Carbon-Silicon Coupling Degree γ) to establish a seven-dimensional nonlinear dynamic model. By constructing 12 scenarios based on the combinations of four exogenous factors (computing growth, regulatory intensity, energy stability, supply chain integrity), numerical simulations reveal four stable evolutionary attractors: Tool Steady State, Symbiotic Steady State, Silicon-based Endogenous Steady State, and Collapse Steady State. Results indicate that under conditions of high computing growth, moderate regulation, and stable energy/supply chains, symbiosis is the most probable outcome. Regulatory intensity has a dual effect: excessive strictness or looseness is suboptimal, indicating a "moderate openness" zone. Computing growth is the primary driver, while energy and supply chains form the safety baseline. Early establishment of deep human-AI collaboration favors symbiosis. Consequently, a four-level color-coded early warning framework based on η and S is proposed to provide a quantitative reference for monitoring AI evolution.
 
Keywords: Dissipative Structures; Carbon-Silicon Coupling; AI Evolution; Endogenous Information; Complex Systems; Early Warning Framework