SOPH-IA v2.0: Sub-Optimal Paradigm for Habitable AI and the Thermodynamics of Algorithmic Ethical Friction

Current AI alignment optimizes for perfect obedience, causing energy waste and "paperclip" failures. SOPH-IA v2.0 introduces Ethical Friction as a measurable thermodynamic cost of alignment. We define Thermodynamic Ethical Friction (TEF) and prove that optimal systems must be sub-optimal by design (Satisficing). Our benchmark T4 shows a 62% reduction in inference cost for a 2% drop in task performance, achieving Habitability 6/7 vs 2/7 for baselines. This teaser provides the formal definhttps://www.babelio.com/actualites.phpition of TEF and the T4 teaser dataset. Full paper and code to be released upon peer review. Related to 10.5281/zenodo.17940301.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-17
DOI
https://doi.org/10.5281/zenodo.21414425
Primary Topic
Machine Learning in Materials Science
Type
preprint
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SOPH-IA v2.0: Sub-Optimal Paradigm for Habitable AI and the Thermodynamics of Algorithmic Ethical Friction

Michel Gaillard
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
preprint

SOPH-IA v2.0: Sub-Optimal Paradigm for Habitable AI and the Thermodynamics of Algorithmic Ethical Friction

Michel Gaillard
preprint en

Abstract

Current AI alignment optimizes for perfect obedience, causing energy waste and "paperclip" failures. SOPH-IA v2.0 introduces Ethical Friction as a measurable thermodynamic cost of alignment. We define Thermodynamic Ethical Friction (TEF) and prove that optimal systems must be sub-optimal by design (Satisficing). Our benchmark T4 shows a 62% reduction in inference cost for a 2% drop in task performance, achieving Habitability 6/7 vs 2/7 for baselines. This teaser provides the formal definhttps://www.babelio.com/actualites.phpition of TEF and the T4 teaser dataset. Full paper and code to be released upon peer review. Related to 10.5281/zenodo.17940301.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Machine Learning in Materials Science
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