Sparse autoencoders finally clicked for me, here is the intuition that did it

Spent a week confused about why SAEs matter. The thing that unlocked it: superposition means a single neuron is polysemantic, it fires for many unrelated concepts at once. An SAE re-expresses those dense activations as a much larger but sparse set of features that land closer to one concept each. Suddenly you can isolate a single feature and clamp it up or down and watch the behavior change.