In this video reviewing former OpenAI researcher Nick Cammarata's essay "Neural networks are already clean" [00:23], TheAIGRID explores the argument that neural networks are far less tangled than the prevailing "superposition" theory suggested [05:36]. Instead of cramming random concepts together, models develop features through "recursive generalization," branching outward into related concepts much like a tree [10:32]. Cammarata shows that historic anomalies—such as an early vision neuron firing on both cats and cars—were simply detecting unified geometric patterns like a "V" shape [07:24], and his audits indicate roughly 90% of neurons in models like Qwen have a discernible primary function [13:23]. While peer review is ongoing and frontier models remain massive challenges [20:41], the findings suggest mechanistic interpretability and alignment may be shifting from an intractable mystery into a measurable, normal science [19:24]. [summary assistance by Gemini 3.8 Flash High]
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