Yeah, but that kills the implied hope of building a better model for cheaper. Like this you'll always have a ceiling of being a bit worse then the openai models.
The logic doesn't exactly hold, it is like saying that a student is limited by their teachers. It is certainly possible that a bad teacher will hold the student back, but ultimately a student can lag or improve on the teacher without only a little extra stimulus.
They probably would need some other source of truth than an existing model, but it isn't clear how much additional data is needed.
Don't forget that this model probably has far less params than o1 or even 4o. This is a compression/distillation, which means it frees up so much compute resources to build models much powerful than o1. At least this allows further scaling compute-wise (if not in the amount of, non-synthetic, source material available for training).
