One foundation, many buildings

The split mattered because the expensive part only had to happen once.

That single costly base model can then be fine-tuned by dozens of different teams into dozens of different tools, none of them paying the enormous pretraining bill again.

The is the point. Pretraining a large model can run for months across thousands of processors. Fine-tuning one for a particular job can take a single machine and an afternoon, on a few thousand examples rather than a large fraction of the written internet.

One foundation, many buildings on top. It is why a handful of base models sit underneath a vast sprawl of AI products, and why a small team can ship something built on training it could never have paid for.

One foundation, many buildings

The split mattered because the expensive part only had to happen once.

That single costly base model can then be fine-tuned by dozens of different teams into dozens of different tools, none of them paying the enormous pretraining bill again.