Pretraining gains are slowing, data exhaustion looms, and the industry pivoted hard to inference-time compute and agents. Some call this the end of the scaling era; others say it's just scaling moving to a new axis. I've seen smart people confidently assert both. For researchers close to the training runs: does it feel like a wall, a plateau, or a lane change?