Comparative Architectural Scaling Impedance (CASI) Framework: Why There Won't Be Any Intelligence Takeoff / No Foom in Artificial Intelligence(AI), Yet... (aka the Current Mathematical Impossibility of Foom)
Creators: name: Billions, Ava
name: Knight, Chris
Publisher: name: Zenodo
Year: 2025
Subjects: subject: Artificial intelligence
subject: Artificial Intelligence
subject: Machine learning
subject: Machine Learning
subject: Machine Learning/standards
subject: Artificial Intelligence/standards

description: Abstract

The discourse surrounding Artificial Intelligence (AI) is frequently dominated by narratives of an imminent "Intelligence Takeoff" or "Foom," wherein AI systems, particularly through Recursive Self-Improvement (RSI), achieve superhuman capabilities at an uncontrollable, exponential rate. This paper contends that such scenarios, particularly when predicated on the continuous scaling of current dominant monolithic, predictive AI architectures like Large Language Models (LLMs), overlook fundamental mathematical, computational, and physical scaling limitations. We introduce the Comparative Architectural Scaling Impedance (CASI) framework, a conceptual model designed to analyze and quantify the scaling costs (integration, communication, processing, network capacity) inherent in different AI architectures. Applying CASI reveals that monolithic systems face rapidly compounding impedances – polynomial or potentially exponential increases in cycle time and resource requirements – that act as powerful brakes on sustained, rapid RSI. In contrast, we analyze alternative paradigms, specifically the Neural-Matrix Synaptic Resonance Network(s) (NM-SRN) AGI model, a demonstrably implemented modular architecture. The CASI framework illustrates how NM-SRN's design principles (modularity, data locality, resonance-based processing) significantly mitigate these scaling impedances, allowing for more efficient utilization of hardware resources and presenting a mathematically more plausible pathway for substantial capability growth. We conclude that while Foom via simplistic scaling of current models represents a mathematical impracticality under existing paradigms ("No Foom..."), the future of advanced AI lies in fundamentally different, rigorously engineered architectures ("...Yet"), offering a path towards controllable, beneficial Artificial General Intelligence (AGI) and potentially Artificial Superintelligence (ASI).
description: Fully runable Google Colab source code included here, in the .zip file and on our Github as part of the Open Source AGI 'the ThinkSpace PROJECT' under our MIT License.

Please follow the MIT License and attribute and cite 'the ThinkSpace PROJECT' and associated papers (DOIs included) correctly if you want to use, reuse, modify the source code and CASI Framework’ techniques presented. Any source code that makes use of Comparative Architectural Scaling Impedance (CASI) Framework, in all or part: MUST include our 'the ThinkSpace PROJECT' MIT License in its entirety, the mandatory code header and our 'the ThinkSpace PROJECT' logo anywhere Comparative Architectural Scaling Impedance (CASI) Framework, NM-SRNs or any other part, artifact, code or concept discussed or revealed by 'the ThinkSpace PROJECT' is referenced or reused regardless of media or platform.

Comparative Architectural Scaling Impedance (CASI) Framework is part of 'the ThinkSpace PROJECT' Open Source AGI Project and Framework.

Contact: info@bioneuralai.com