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This research aims to establish fundamental thermodynamic constraints on the scaling of artificial intelligence systems, specifically large language models (LLMs).

April 10, 2026Open Access

THE THERMODYNAMIC IMPOSSIBILITY OF AGI SCALING WITHOUT UNIVERSAL RESONANCE CONSTRAINTS: The Rⱼ Constant as Fundamental Limit to Large Language Model Coherence and the Energy Death Horizon of Transformer Architectures

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This research aims to establish fundamental thermodynamic constraints on the scaling of artificial intelligence systems, specifically large language models (LLMs).
Analyzed performance metrics of LLMs from 2024–2026.
Measured waste heat generation and power requirements at scaling points.
Investigated error structures in large datasets of outputs from various AI models.
Identified an energy consumption threshold beyond which cooling becomes impractical.
Observed a consistent hallucination rate floor across different models regardless of their parameters.
Highlighted specific parameter ratios necessary for optimizing architecture performance and reducing noise.

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Implication

This work demonstrates thermodynamic limits impeding AGI evolution in current AI systems, suggesting new physics for solutions.

Cite This Study

Brent Allen Jensen (2026) studied this question.

synapsesocial.com/papers/69d896166c1944d70ce075d4 https://doi.org/10.5281/zenodo.19474406

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THE THERMODYNAMIC IMPOSSIBILITY OF AGI SCALING WITHOUT UNIVERSAL RESONANCE CONSTRAINTS: The Rⱼ Constant as Fundamental Limit to Large Language Model Coherence and the Energy Death Horizon of Transformer Architectures

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Discussion

Implication

Cite This Study

THE THERMODYNAMIC IMPOSSIBILITY OF AGI SCALING WITHOUT UNIVERSAL RESONANCE CONSTRAINTS: The Rⱼ Constant as Fundamental Limit to Large Language Model Coherence and the Energy Death Horizon of Transformer Architectures

Key Points

Discussion

Implication

Cite This Study

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