The Misguided Quest for Mechanistic AI Interpretability

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Written with Laura Hiscott, this essay argues that mechanistic interpretability — reverse-engineering models neuron by neuron and circuit by circuit — rests on a mistaken premise, because model behavior emerges from vast numbers of nonlinear interactions rather than from discrete, legible mechanisms. Surveying more than a decade of feature visualizations, saliency maps, and sparse autoencoders, the authors contend the field has not delivered reliable insight, and make the case for top-down methods like representation engineering that read higher-level patterns across many neurons, as scientists do in other complex systems.