Anthropic's recent study addresses the polysemantic neuron problem in language models by employing sparse autoencoders to decompose transformer activations into more interpretable features, using dictionary learning on eight billion activation samples from GPT-2 Small's layer 6. This method, which involves a 16× expansion of the hidden size and an L1 sparsity penalty, successfully transforms complex neuron activations into nearly 15,000 distinct, interpretable features, with 70% of them clearly mapping to single concepts like Arabic script or DNA motifs. The research demonstrates that these features allow practitioners to steer model outputs, audit reasoning, and enhance language model safety through more manageable and monitorable features compared to traditional neuron-level approaches. The study is validated by human agreement, decoder-row alignment, resilience against adversarial tests, and causal interventions, indicating that these semantic building blocks appear consistently in larger transformers, suggesting a scalable, universal vocabulary of features. While the approach offers significant advancements in interpretability and control, it also highlights limitations such as overlapping features and the challenge of scaling to larger models without excessive computation. The findings are complemented by practical insights shared in Anthropic's Chain of Thought podcast, offering strategies for real-world implementation and further exploration into the mechanics of interpretable feature discovery.