Goodfire's Nick Cammarata: neural networks are "already clean" in a possible interpretability breakthrough
tszzl · x · 2026-10-15
Nick Cammarata (Goodfire) published a long essay arguing mechanistic interpretability is far more tractable than the field believed. The canonical polysemanticity/superposition evidence was misread — the pattern is actually 'recursive generalization' — and with modern agentic tools most neurons in models up to trillions of parameters are legible, so networks can be studied directly without untangling. Commenters like tszzl call it a potentially historic moment for alignment. Cammarata previously found only 2% of neurons legible in OpenAI's early automated interpretability work.
Related event: Ex-OpenAI Interpretable Lead Claims Neural Networks Are Already Clean(5 posts)→
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