The article introduces weight-sparse transformers (models where most weights are zero) to make neural circuits interpretable. These models reveal clear, human-understandable algorithms for language tasks. Sparsity trades off raw capability for clarity, allowing researchers to fully trace mechanisms inside networks and bridge them to dense models for transparency in AI reasoning.