Generalized Dissipative Catalysis Theory: A Unified Physical Framework from the Origin of Life to Artificial Intelligence

This article introduces the Generalized Dissipative Catalysis (GDC) theoretical framework, whose core is redefining the essence of life as inheritable constraints that can accelerate, broaden, or stabilize free energy dissipation pathways. This theory unifies the underlying logic of enzymes, genomes, nervous systems, social structures, and even artificial intelligence, providing a new perspective for understanding the origin and evolution of life and the nature of intelligence.

Traditional biology regards replication as the core feature of life, but faces the problem of the formation of the first replicable molecule before the birth of life. The GDC theory proposes a subversive perspective: the primitive feature of life is not replication, but inheritable constraints; replication is a derived mechanism whose role is to preserve catalytic organizational structures across time.

GDC is based on Prigogine's concept of 'dissipative structures' (open systems far from equilibrium form self-organized structures through energy and material exchange), and proposes 'dissipative catalytic constraints': structural features that can reduce the activation energy barrier of energy dissipation pathways, expand dissipation channels, or stabilize specific dissipation modes. These constraints create conditions for the emergence of life.

GDC has universality and provides unified explanations for multi-scale phenomena:

1. Enzymes: Biological catalysts whose three-dimensional structural constraints accelerate specific energy dissipation reactions;
2. Genomes: Their core role is to maintain the stability of dissipative catalytic constraints, encoding and transmitting structures that sustain specific dissipation modes;
3. Nervous systems: Complex dissipative catalytic devices where perception, learning, and decision-making are processes of finding more effective energy dissipation pathways, and neural plasticity adjusts constraint parameters;
4. Social structures: Macroscopic dissipative catalytic constraints that coordinate individual behaviors to create large-scale stable energy dissipation patterns;
5. AI: A form of information dissipative catalysis where training is the process of finding constraint configurations that effectively extract information structures from data, and emergent capabilities may stem from the richness and stability of information dissipation pathways.

GDC reveals a fundamental trade-off across all scales: speed vs. stability. Efficient constraints accelerate energy flow but increase the risk of fluctuations; systems need to balance dissipation efficiency and structural stability. This explains why evolution is not always optimal and the 'robust yet fragile' characteristics of complex systems.

GDC provides a new understanding of genetic repair mechanisms: repair mechanisms (DNA repair, protein quality control) are active processes to maintain dissipative catalytic constraints; inheritance is not only the transmission of genes but also the reconstruction of similar dissipative catalytic structures by offspring. This perspective inspires understanding of epigenetic inheritance, cultural inheritance (memes), and technological inheritance—their common point is to serve the cross-generational transmission of constraints.

GDC's implications for AI research:

1. Re-defining intelligence: The ability to find efficient dissipation pathways in complex environments, requiring attention to energy efficiency and structural stability;
2. Path to general AI: Building systems that dynamically adjust constraint structures and adaptively balance speed and stability (involving meta-learning, continuous learning, etc.);
3. Focus on AI's ecological niche: Embedding into larger energy-information flow networks, and understanding social impacts requires a broader perspective.

The GDC theory is in its early stage and has shown potential to integrate research in biology, physics, cognitive science, and AI. It redefines the essence of life as dissipative catalytic constraints and provides a unified language to understand complex phenomena. Questions to explore: experimental verification of the framework, generating testable predictions, and the relationship with other complex system theories (free energy principle, causal emergence). Regardless of the outcome, GDC broadens the horizon for thinking about life, intelligence, and evolution.