this will change as inference demand increases (which is happening right now faster than many people expected)

At the same time, the training paradigm being scaled, Reinforcement Learning, is significantly less data-efficient than next-token prediction. You basically need to run an agent for minutes (or longer if you want good long-horizon performance), only to give it a binary pass/fail - one bit of information.

Inference compute is definitely scaling fast, but to scale RL, training and R&D compute also needs to scale hard. I don't think it's obvious that inference will overtake R&D/training, unless there's a reputable source that states that.

do you have some ref?