Ex-OpenAI researcher says 90% of LLM neurons have one clear job
Nick Cammarata, who helped found OpenAI's interpretability team and now works at Goodfire, published a long essay titled "Neural Networks Are Already Clean," arguing that neural networks are not as entangled as the widely held superposition hypothesis suggested, so researchers can study individual neurons directly instead of first decomposing features with tools such as sparse autoencoders. He estimates that in a preliminary review of Qwen3-32B roughly 90% of neurons have a single main job at each polarity, about 7% have two related jobs, and 2%–4% remain unexplained, with similar patterns reported in trillion-parameter models including Kimi K3. The essay also describes agent tools that inspect neurons, propose explanations and design falsification tests, though he stops short of declaring interpretability solved and notes that attention mechanisms and full model auditing remain open.
If the claim holds up, AI safety work could shift from building complex feature-decomposition tools toward directly auditing model internals.