Before it
One network had to learn every part of a task with the same parameters.
The problem
Different regions of a problem want different specialists, and one dense model spends all its capacity on all of it.
What it introduced
Several expert subnetworks plus a gating network that decides which expert handles which input.
Why it matters
This is the ancestor of every sparse mixture-of-experts model, including Mixtral, DeepSeek and Llama 4.
What modern models inherit
Conditional computation: activate a fraction of the parameters per token.
What came next
Dormant for 25 years until Shazeer scaled it in 2017.