Transfer learning takes a model trained on one task and repurposes it for a different but related task, saving enormous amounts of data and compute. A model pre-trained on millions of general images can be fine-tuned with just a few hundred medical scans to detect diseases. This is why modern AI development starts with pre-trained foundation models rather than training from scratch, dramatically lowering the barrier to entry.