EnergeticaX Journal

A systematic examination of contemporary AI architectures and the emerging cognitive specialization among large language models, analysing how architectural differences produce distinct reasoning profiles and benchmark divergence.

Re-frames investment bubbles not as market failures but as adaptive capital search mechanisms under deep technological uncertainty, proposing a new model of capital allocation dynamics in transformative technology cycles.

Introduces the concept of capital as a search function in uncertain technological environments, extending classical capital theory with cognitive-energetic principles to explain non-linear allocation patterns.

Deconstructs the popular belief in hidden prompt commands, analysing how semantic compression and meaning drift undermine reliable human-AI communication and proposing compatibility metrics grounded in cognitive science.

Examines the structural gap between human and AI understanding, arguing that what appears as comprehension in AI systems is a probabilistic simulation of meaning — with profound implications for governance, education and institutional design.

Analyses the geopolitical and epistemic consequences of diverging national AI ecosystems, introducing the concept of cognitive fragmentation and its implications for global coordination, semantic alignment and institutional resilience.

Presents the noem Compatibility Index — a quantitative metric for measuring alignment between human and artificial cognitive systems. Co-authored with AI under the Ex Lumine Veritas framework, establishing formal compatibility scoring grounded in cognitive-energetic principles.

Proposes a thermodynamic model of decision-making that integrates behavioral economics with entropy-based cognitive constraints, offering a unified theory of choice that accounts for energy expenditure in meaning formation and option evaluation.

Applies the second law of thermodynamics to cognitive systems, demonstrating that reasoning, learning and meaning formation are subject to entropy constraints — with implications for AI design, education and institutional knowledge management.

Defines and formalises the noem Index as a scalar measure of cognitive entropy applicable to both human and artificial agents. Provides mathematical derivation, validation methodology and use cases in AI evaluation and educational assessment.

Presents the full theoretical architecture of Noem Theory — an entropy-based cognitive framework that unifies meaning formation, decision dynamics and human-AI interaction within a single formal system.