OpenAI interpretability team creator says large model black boxes are almost readable; about 90% of neurons in Qwen3-32B have a single function

Nick Cammarata, creator of OpenAI's interpretability team, said in a long post that with modern agent tools, trillion-parameter large models are almost readable, and that the superposition hypothesis, which held that neural network representations are entangled, is a misunderstanding. Re-examining a neuron in InceptionV1 that responds to both cat faces and car fronts, he believes the two share a V-shaped feature, and that neurons are more like verbs that distinguish patterns than nouns that label objects. The author's preliminary review of Qwen3-32B shows that about 90% of neurons have one main job at each of the positive and negative poles, about 7% have two, and 2% to 4% are not yet understood; conclusions are similar for dense models and MoE models. The author says he produced the first known attribution map for a trillion-parameter model on Kimi K3, and when re-examining GPT-2 XL with new tools, about 70% of neurons are also roughly one thing. Agents are used to inspect neuron samples, propose explanations, trace upstream and downstream connections, and design counterexample experiments.