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Fundamental Computational Limits and the Trajectory of Technological Development

Authors: Zelenka, David D.;

Fundamental Computational Limits and the Trajectory of Technological Development

Abstract

We synthesize three independent frameworks-the P != NP theorem from operational gradient theory, Threaded Mind's embodied consciousness model, and thermodynamic energy constraints to argue that Artificial General Intelligence (AGI) faces fundamental rather than merely engineering limitations. Drawing on the Intrinsic Operational Gradient Theorem (IOGT), we demonstrate that the computational asymmetry between construction and verification is not algorithmic but structural, arising from the non-invertibility of information-processing operations. Threaded Mind theory reveals that human intelligence emerges not from isolated neural computation but from recursive memory dynamics distributed across brain, body, relationships, history, and environmental coupling-a cognitive ecology requiring exponential parallelism to replicate digitally. Finally, we examine thermodynamic bounds: the absence of low-temperature fusion (cold fusion) and the irreducible energy costs of computation impose hardware limits that prevent digital systems from matching biological efficiency. Together, these constraints suggest that human-level AGI may be physically impossible within polynomial resource bounds, with profound implications for technological trajectories, economic assumptions, and the unique status of biological intelligence.

Keywords

Artificial Intelligence/economics, Mathematical Computing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green