Part 5/12:
A notable insight involves "distillation"—a process where large, inefficient "frontier" models are used internally to train smaller, faster, and more efficient models. OpenAI, the speaker suggests, is shifting away from releasing massive models publicly. Instead, they develop high-capacity models internally and then extract distilled versions for end-users. This approach maintains model quality while reducing inference costs and improving user experience.