The labs' core AGI bet is that training on enough verifiable, containerized RL environments will produce agents capable of open-ended problem-solving for weeks on end. Skeptics argue the fundamental deficits — data inefficiency, no continual learning — will simply be steamrolled by scale, just as compute steamrolled NLP.