Training a capable model from nothing needs vast data and computation. Transfer learning starts from a model already trained on a broad task and adapts it to a narrower one, reusing the general understanding it has already acquired.
This makes it possible to build a good specialised model with far less data and effort, since only the final adjustment is task-specific. It is one of the main reasons that useful models can be built by teams without the resources to train one from the beginning.