Part I: Foundations · llm_ai · Proposed

Landauer Principle as Thermodynamic Anchor of Informational Irreversibility

What the data actually shows

Irreversible computational operations (erasure, overwriting, catastrophic forgetting in learning) always dissipate heat at Landauer’s limit, yet AI scaling laws and biological memory models rarely incorporate this fundamental thermodynamic cost of irreversibility as a hard constraint.

Why this is necessary

Any consistent theory of a self-improving information processor must include the minimal energy cost of irreversible information processing. Without it, calculations of maximum sustainable information capacity (I_max) are incomplete; the polarity between reversible and irreversible computation defines a fundamental gradient that shapes all real attractors.

What current approaches miss

Information theory and thermodynamics developed in separate silos for decades. Landauer’s result was viewed as an engineering curiosity rather than a foundational constraint on any self-improving computational system, including biological and civilizational ones.

If this is right

Imposes hard physical bounds on sustainable AI scaling and training efficiency. Guides design of reversible or low-erasure architectures. Explains part of the LLM reasoning wall as a thermodynamic depth limit. Informs energy-aware institutional and personal epistemic practices.

Documented theorem joins

Join table only. No invented bijection.

• Th.14 Limits of Optimization Theorem
• Th.15 Entropy as Resource Theorem
• Th.49 Finitude as Optimization Mechanism Theorem
• Th.50 Death and Renewal Cycles Theorem
• Th.51 Memory as Holographic Necessity Theorem
• Th.52 Ethics of Memory and Forgetting Theorem