Phase 1: Large-scale pretraining → build broad pattern recognition
Phase 2: Target-domain retraining → adapt that foundation to a smaller, high-value dataset
The output of Phase 1 becomes the input for Phase 2, which is why upstream model choice directly shapes downstream performance, cost, and data efficiency. If the source domain is too far from the target, adaptation value drops. If the match is strong, teams gain faster deployment, lower labeling burden, and better results than training from scratch.
The strategic lesson is clear: competitive AI performance comes from designing the whole learning pipeline, not optimizing one isolated stage. Strong AI outcomes depend on systemic thinking across connected stages.