Comment by Loquebantur

They have maxed out the base data with the given model architecture and scale "intelligence" through inference-based RL.

Notably, in order to achieve a specific quality, the model has to "reason" for every prompt proportional to the training inference.

This is arguably the case of diminishing returns being reached, as answer token prices saturate the willingness of customers to pay.

They've run into a wall.