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arXiv:2512.20264astro-ph.IM

The AI Scaling Wall of Diminishing Returns: Of LLMs, Electric Dogs, and General Relativity

Hemant Shukla

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Abstract

LLMs are hitting the scaling wall - compute grows 10-100x while accuracy barely moves. This note quantifies the slowdown and argues that the next leap in AI will come not from bigger models, but from smarter, more efficient ones.

arXiv comments
Journal reference
DOI
Subjects
astro-ph.IMInstrumentation and Methods for Astrophysics physics.comp-phComputational Physics

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  1. Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data 1 comment

    • >The scaling laws hold that a language model grows more capable with more parameters and more training data. Which is a choice, not a "law": https://arxiv.org/abs/2510.13786 https://www.alphaxiv.org/abs/2512.20264 https://arxiv.org/abs/2607.05155

      cyanydeezView on HNParent context

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